Free AWS MLA-C01 Practice Questions & Answers 2026 Part2

Question #1

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #2

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. ncrease the temperature parameter and the top_k parameter

B. ncrease the temperature parameter

C. ecrease the temperature parameter

D. ecrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #3

An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node, core nodes, and task nodes on On-Demand Instances

B. Run the primary node, core nodes, and task nodes on Spot Instances

C. Run the primary node on an On-Demand Instance

D. Run the primary node and core nodes on On-Demand Instances

View answer

Correct Answer: D

Question #4

A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs

B. Use processing steps in SageMaker Pipelines

C. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running

D. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order

View answer

Correct Answer: C

Question #5

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #6

A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.The company needs to use the dataset in a solution to determine if a model can predict the target variable.Which solution will provide this information with the LEAST development effort?

A. Create a new model by using Amazon SageMaker Autopilot

B. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation

C. Configure Amazon Macie to analyze the dataset and to create a model

D. Select a model from Amazon Bedrock

View answer

Correct Answer: A

Question #7

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. Create a SageMaker real-time inference endpoint

B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #8

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #9

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #10

A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account. An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses. Which solution will meet these requirements? S3 Gateway Endpoint: Allows private access to S3 from within a VPC without requiring a public IPv4 address, ensuring that data transfer between the primary and secondary accounts is secure and private. Bucket Policy Update: The S3 bucket policy in the secondary account must explicitly allow access from the primary account's IAM principals to provide the necessary permissions. Interface VPC Endpoints: Required for private communication between the VPC and Amazon SageMaker and Amazon Redshift services, ensuring the solution operates without public internet access. This configuration meets the requirement to avoid public IPv4 addresses and allows secure and private communication between the accounts.

A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account

B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account

C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account

D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account

View answer

Correct Answer: D

Question #11

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #12

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #13

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #14

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #15

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #16

Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.Which action will meet this requirement with the LEAST operational overhead?

A. Use AWS Glue to transform the categorical data into numerical data

B. Use AWS Glue to transform the numerical data into categorical data

C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data

D. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data

View answer

Correct Answer: C

Question #17

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. Create a SageMaker real-time inference endpoint

B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #18

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #19

Case StudyA company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, acentral model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company needs to use the central model registry to manage different versions of models in the application.Which action will meet this requirement with the LEAST operational overhead?

A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model

B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version

C. Use the SageMaker Model Registry and model groups to catalog the models

D. Use the SageMaker Model Registry and unique tags for each model version

View answer

Correct Answer: C

Question #20

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #21

A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.The company needs to implement a scalable solution on AWS to identify anomalous data points.Which solution will meet these requirements with the LEAST operational overhead?

A. Ingest real-time data into Amazon Kinesis data streams

B. Ingest real-time data into Amazon Kinesis data streams

C. Ingest real-time data into Apache Kafka on Amazon EC2 instances

D. Send real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue

View answer

Correct Answer: A

Question #22

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #23

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #24

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #25

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #26

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.Which solutions will mitigate this problem? (Choose two.)

A. Enable early stopping on the model

B. Increase dropout in the layers

C. Increase the number of layers

D. Increase the number of neurons

E. Investigate and reduce the sources of model bias

View answer

Correct Answer: AB

Question #27

Which algorithm is most suitable for a use case that requires clustering unstructured text data?

A. K-Means

B. XGBoost

C. Linear Regression

D. Random Forest

View answer

Correct Answer: A

Question #28

Case StudyA company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company is experimenting with consecutive training jobs.How can the company MINIMIZE infrastructure startup times for these jobs?

A. Use Managed Spot Training

B. Use SageMaker managed warm pools

C. Use SageMaker Training Compiler

D. Use the SageMaker distributed data parallelism (SMDDP) library

View answer

Correct Answer: B

Question #29

An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region.Which solution will meet this requirement with the LEAST development effort?

A. Use Amazon S3 to make a copy of the model

B. Create a resource-based IAM policy

C. Use AWS DataSync to replicate the model from Account A to Account B

D. Create an AWS Site-to-Site VPN connection between Account A and Account B to transfer the model

View answer

Correct Answer: B

Question #30

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model

A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model

B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version

C. Use the SageMaker Model Registry and model groups to catalog the models

D. Use the SageMaker Model Registry and unique tags for each model version

View answer

Correct Answer: C

Question #31

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.Which solutions will mitigate this problem? (Choose two.)

A. Enable early stopping on the model

B. Increase dropout in the layers

C. Increase the number of layers

D. Increase the number of neurons

E. Investigate and reduce the sources of model bias

View answer

Correct Answer: AB

Question #32

An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference

A. Apply statistics from a well-known dataset to normalize the production samples

B. Keep the min-max normalization statistics from the training set

C. Calculate a new set of min-max normalization statistics from a batch of production samples

D. Calculate a new set of min-max normalization statistics from each production sample

View answer

Correct Answer: B

Question #33

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #34

Case StudyA company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company needs to use the central model registry to manage different versions of models in the application.Which action will meet this requirement with the LEAST operational overhead?

A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model

B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version

C. Use the SageMaker Model Registry and model groups to catalog the models

D. Use the SageMaker Model Registry and unique tags for each model version

View answer

Correct Answer: C

Question #35

An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node, core nodes, and task nodes on On-Demand Instances

B. Run the primary node, core nodes, and task nodes on Spot Instances

C. Run the primary node on an On-Demand Instance

D. Run the primary node and core nodes on On-Demand Instances

View answer

Correct Answer: D

Question #36

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #37

An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node, core nodes, and task nodes on On-Demand Instances

B. Run the primary node, core nodes, and task nodes on Spot Instances

C. Run the primary node on an On-Demand Instance

D. Run the primary node and core nodes on On-Demand Instances

View answer

Correct Answer: D

Question #38

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #39

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations

B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations

C. Use Amazon Comprehend to analyze sentiments of the chat conversations

D. Use random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #40

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #41

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #42

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations

B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations

C. Use Amazon Comprehend to analyze sentiments of the chat conversations

D. Use random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #43

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. Create a SageMaker real-time inference endpoint

B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #44

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations

B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations

C. Use Amazon Comprehend to analyze sentiments of the chat conversations

D. Use random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #45

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model

A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job

B. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image

C. Use AWS Glue Data Quality to monitor bias

D. Use SageMaker notebooks to compare the bias

View answer

Correct Answer: A

Question #46

A company runs an ML model on Amazon SageMaker AI. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs.Which solution will prevent SageMaker AI from collecting metadata from the training jobs?

A. ncrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key

B. nsure that training jobs are running in a private subnet in a custom VPC

C. pt out of metadata tracking for any training job that is submitted

D. econfigure the training jobs to use only AWS Nitro instances

View answer

Correct Answer: C

Question #47

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #48

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #49

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #50

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. se Amazon Rekognition to analyze sentiments of the chat conversations

B. rain a Naive Bayes classifier to analyze sentiments of the chat conversations

C. se Amazon Comprehend to analyze sentiments of the chat conversations

D. se random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #51

Case StudyA company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, acentral model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.Which solution will meet this requirement?

A. Use SageMaker Experiments to facilitate the approval process during model registration

B. Use SageMaker ML Lineage Tracking on the central model registry

C. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval

D. Use SageMaker Pipelines

View answer

Correct Answer: D

Question #52

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #53

An ML engineer develops a neural network model to predict whether customers will continue to subscribe to a service. The model performs well on training data. However, the accuracy of the model decreases significantly on evaluation data.The ML engineer must resolve the model performance issue.Which solution will meet this requirement?

A. emove dropout layers from the neural network

B. rain the model for longer by increasing the number of epochs

C. enalize large weights by using L1 or L2 regularization

D. apture complex patterns by increasing the number of layers

View answer

Correct Answer: C

Question #54

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. Create a SageMaker real-time inference endpoint

B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #55

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations

B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations

C. Use Amazon Comprehend to analyze sentiments of the chat conversations

D. Use random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #56

An ML engineer needs to use an ML model to predict the price of apartments in a specific location.Which metric should the ML engineer use to evaluate the model's performance?

A. Accuracy

B. Area Under the ROC Curve (AUC)

C. F1 score

D. Mean absolute error (MAE)

View answer

Correct Answer: D

Question #57

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations

B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations

C. Use Amazon Comprehend to analyze sentiments of the chat conversations

D. Use random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #58

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #59

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #60

Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.Which solution will meet this requirement with the LEAST operational effort?

A. Use Amazon Athena to identify patterns that contribute to the imbalance

B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset

C. Use AWS Glue DataBrew built-in features to oversample the minority class

D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class

View answer

Correct Answer: D

Question #61

Case Study -A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company is experimenting with consecutive training jobs.How can the company MINIMIZE infrastructure startup times for these jobs?

A. se Managed Spot Training

B. se SageMaker managed warm pools

C. se SageMaker Training Compiler

D. se the SageMaker distributed data parallelism (SMDDP) library

View answer

Correct Answer: B

Question #62

What scaling policy would you use for a model endpoint with fluctuating but predictable daily traffic patterns?

A. On-demand scaling

B. Time-based scaling

C. Manual scaling

D. Fixed scaling

View answer

Correct Answer: b

Question #63

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #64

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. Create a SageMaker real-time inference endpoint

B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #65

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #66

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations

B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations

C. Use Amazon Comprehend to analyze sentiments of the chat conversations

D. Use random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #67

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #68

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #69

A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.The company needs to implement a scalable solution on AWS to identify anomalous data points.Which solution will meet these requirements with the LEAST operational overhead?

A. ngest real-time data into Amazon Kinesis data streams

B. ngest real-time data into Amazon Kinesis data streams

C. ngest real-time data into Apache Kafka on Amazon EC2 instances

D. end real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue

View answer

Correct Answer: A

Question #70

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #71

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #72

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #73

A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.Which update to the network configuration will meet this requirement?

A. Create a security group inbound rule to deny traffic from the specific IP address

B. Create a network ACL inbound rule to deny traffic from the specific IP address

C. Create a shadow variant for the domain

D. Create a VPC route table to deny inbound traffic from the specific IP address

View answer

Correct Answer: B

Question #74

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #75

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #76

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #77

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #78

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #79

A model deployed to production shows significant performance degradation over time. You suspect that the input data distribution has shifted compared to the training data.What actions should you take to diagnose and address the issue?(Choose three)

A. Change the model algorithm to one more robust to data drift

B. Retrain the model using updated data

C. Reduce the batch size for inference

D. Evaluate the performance metrics for the updated test dataset

E. Use SageMaker Model Monitor to detect data drift

View answer

Correct Answer: bde

Question #80

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter

B. Increase the temperature parameter

C. Decrease the temperature parameter

D. Decrease the temperature parameter and the top_k parameter

View answer

Correct Answer: D

Question #81

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #82

An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.Which solution will meet these requirements with the LEAST operational overhead?

A. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket

B. Create a new S3 bucket for processed data

C. Create a new S3 bucket for processed data

D. Create a new S3 bucket for processed data

View answer

Correct Answer: A

Question #83

A company plans to use Amazon SageMaker AI to build image classification models. The company has 6 TB of training data stored on Amazon FSx for NetApp ONTAP. The file system is in the same VPC as SageMaker AI.An ML engineer must make the training data accessible to SageMaker AI training jobs.Which solution will meet these requirements?

A. reate a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system

B. ount the FSx for ONTAP file system as a volume to the SageMaker AI instance

C. reate an Amazon S3 bucket and use Mountpoint for Amazon S3 to link the bucket to FSx for ONTAP

D. reate a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system

View answer

Correct Answer: B

Question #84

An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node, core nodes, and task nodes on On-Demand Instances

B. Run the primary node, core nodes, and task nodes on Spot Instances

C. Run the primary node on an On-Demand Instance

D. Run the primary node and core nodes on On-Demand Instances

View answer

Correct Answer: D

Question #85

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

Question #86

An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata.Which solution will meet these requirements with the LEAST manual effort?

A. reate an ETL pipeline by using Amazon Athena integrated with AWS Step Functions

B. se AWS Glue to run the ETL job

C. aunch an Amazon EC2 instance that includes the scikit-learn library to run the ETL job

D. reate an Amazon SageMaker Data Wrangler flow to run the ETL job

View answer

Correct Answer: B

Question #87

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. reate a SageMaker real-time inference endpoint

B. eploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. nstall SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. se Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #88

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #89

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. Create a SageMaker real-time inference endpoint

B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #90

An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node, core nodes, and task nodes on On-Demand Instances

B. Run the primary node, core nodes, and task nodes on Spot Instances

C. Run the primary node on an On-Demand Instance

D. Run the primary node and core nodes on On-Demand Instances

View answer

Correct Answer: D

Question #91

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.How should the company deploy the model into production to meet these requirements?

A. Create a SageMaker real-time inference endpoint

B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster

C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster

D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences

View answer

Correct Answer: A

Question #92

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_classifier for the predictor_type hyperparameter.What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero

B. Increase the number of training epochs

C. Increase the value of the target_precision hyperparameter

D. Change the value of the predictor_type hyperparameter to regressor

View answer

Correct Answer: C

Question #93

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.Which change to the architecture will meet these requirements?

A. Use zero buffering in the Firehose stream

B. Replace the Firehose stream with an AWS DataSync task

C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds

D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue

View answer

Correct Answer: A

Question #94

Case studyAn ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.Which action will meet this requirement with the LEAST operational overhead?

A. Use AWS Glue to transform the categorical data into numerical data

B. Use AWS Glue to transform the numerical data into categorical data

C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data

D. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data

View answer

Correct Answer: C

Question #95

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangler

B. Use Amazon SageMaker notebooks for each data processing step

C. Process data at each step by using AWS Lambda functions

D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps

View answer

Correct Answer: D

Question #96

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations

B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations

C. Use Amazon Comprehend to analyze sentiments of the chat conversations

D. Use random forests to classify sentiments of the chat conversations

View answer

Correct Answer: C

Question #97

Case Study -A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company needs to use the central model registry to manage different versions of models in the application.Which action will meet this requirement with the LEAST operational overhead?

A. reate a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model

B. se Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version

C. se the SageMaker Model Registry and model groups to catalog the models

D. se the SageMaker Model Registry and unique tags for each model version

View answer

Correct Answer: C

Question #98

A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

A. Spot Instances

B. Reserved Instances

C. On-Demand Instances

D. Dedicated Instances

View answer

Correct Answer: A

Question #99

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected

B. Use Amazon SageMaker Ground Truth for data labeling

C. Deploy models by using AWS Lambda functions

D. Use AWS Trainium instances for training

E. Use PyTorch or TensorFlow with the distributed training option

View answer

Correct Answer: AD

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