DON'T WANT TO MISS A THING?

Certification Exam Passing Tips

Latest exam news and discount info

Curated and up-to-date by our experts

Yes, send me the newsletter

Free AWS MLA-C01 Practice Questions & Answers 2026 Part4

Are you preparing for the AWS MLA-C01 certification exam? SPOTO offers the AWS MLA-C01 Premium File; all questions are aligned with the latest exam content and come with expert-provided answers. Our question banks cover the latest question types, core concepts, and detailed explanations, helping you familiarize yourself with the exam format and difficulty level. Whether you are reviewing core concepts or simulating a real exam environment, these resources will rapidly boost your confidence and readiness.
For over two decades, SPOTO has successfully helped numerous IT professionals secure their ideal positions at Fortune 500 companies. Download now to start practicing efficiently and ensure a high score on the actual exam. Don't miss this opportunity to pass your certification exam with ease!
Take other online exams
Question #1
An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary features. The ML engineer must reduce the overfitting and the impact of the unnecessary features.Which solution will meet these requirements?
A. ecrease the number of training iterations
B. se SageMaker Debugger to apply L1 regularization to the running model
C. pply L1 regularization to the training data
D. ncrease the number of training iterations
View answer
Correct Answer: C
Question #2
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.Which solution will meet these requirements?
A. Use an AWS Batch job to process the files and generate embeddings
B. Use a custom Amazon SageMaker AI notebook to run a custom script to generate embeddings
C. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra
D. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket
View answer
Correct Answer: C
Question #3
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 #4
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 #5
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 #6
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 #7
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 #8
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 #9
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 #10
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 #11
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 #12
An ML engineer wants to deploy a workflow that processes streaming IoT sensor data and periodically retrains ML models. The most recent model versions must be deployed to production.Which service will meet these requirements?
A. Amazon SageMaker Pipelines
B. Amazon Managed Workflows for Apache Airflow (MWAA)
C. AWS Lambda
D. Apache Spark
View answer
Correct Answer: A
Question #13
What are common metrics used for evaluating classification models?​(Choose two)
A. Mean Squared Error (MSE)
B. RMSE
C. Recall
D. Precision
View answer
Correct Answer: cd
Question #14
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 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 #15
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 #16
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 #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 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 #19
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 #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 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 #22
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 #23
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry. The data scientists are grouped into three categories: computer vision,
A. Create a custom tag for each of the three categories
B. Create a model group for each category
C. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models
D. Create a Model Registry collection for each of the three categories
View answer
Correct Answer: A
Question #24
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 #25
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 #26
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

View The Updated AWS Exam Questions

SPOTO Provides 100% Real AWS Exam Questions for You to Pass Your AWS Exam!

Get AWS Practice Test

View Answers after Submission

Please submit your email and WhatsApp to get the answers of questions.

Note: Please make sure your email ID and Whatsapp are valid so that you can get the correct exam results.

Email:
Whatsapp/phone number:
Contact Us


Copyright © 2024. Designer by SPOTO Official Website. All Rights Reserved.