Free AWS MLA-C01 Practice Questions & Answers 2026 Part3

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.Which AWS service or feature can aggregate the data from the various data sources?

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.)

An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.What should the ML engineer do to improve the training process?

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?

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 company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI 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 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 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?

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 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 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?

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 run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.Which action will meet this requirement?

A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive. A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database. Which solution will meet these requirements with the LEAST implementation effort?

A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

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 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?

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.After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.Which solution will meet these requirements?

A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine. The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data. Which solution will meet these requirements with the LEAST operational overhead?

A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends.Which algorithm and hyperparameters should the company use to develop the model?

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?

An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.Which solution will meet these requirements?

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 company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.Which solution will meet these requirements?

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 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 nish 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?

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 company is developing a generative AI conversational interface to assist customers with payments. The company wants to use an ML solution to detect customer intent. The company does not have training data to train a model. Which solution will meet these requirements? The key requirement in this scenario is detecting customer intent without having any training data. According to AWS Machine Learning and Generative AI documentation, zero-shot learning is specifically designed for situations where labeled training data is unavailable. Zero-shot learning allows a pre-trained large language model (LLM) to perform tasks it has not been explicitly trained on by leveraging its general knowledge and language understanding. Amazon Bedrock provides fully managed access to foundation models (FMs) and LLMs that support zero-shot and few-shot learning. By using an LLM from Amazon Bedrock, the company can directly infer customer intent from natural language inputs without building, training, or fine-tuning a custom model. This approach is ideal for conversational interfaces where rapid deployment and scalability are required. Option A is incorrect because fine-tuning a sequence-to-sequence (seq2seq) model in Amazon SageMaker JumpStart still requires labeled training data. Since the company explicitly does not have training data, this option does not meet the requirement. Option C is also incorrect because the Amazon Comprehend DetectEntities API is designed for named entity recognition (NER), such as detecting names, dates, locations, or monetary values. It does not perform intent detection and is not suitable for conversational AI intent classification. Option D is partially misleading. While it is technically possible to run an LLM on Amazon EC2, this does not inherently solve the problem of intent detection without training data. Additionally, Amazon Bedrock already abstracts infrastructure management, scaling, and model hosting, making direct EC2 deployment unnecessary and less efficient. Therefore, using an LLM from Amazon Bedrock with zero-shot learning is the most appropriate, scalable, and AWS-recommended solution for intent detection without training data.

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 ## 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 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 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?

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?

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?

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.Which AWS service or feature can aggregate the data from the various data sources?

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 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 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 company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.What could be the reason for the reduced F1 score?

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?

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?

An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.Which metric finding should the ML engineer prioritize the MOST when choosing the model?

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 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 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 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.)

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?

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 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 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 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?

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 ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.Which algorithm should the ML engineer use to meet this requirement?

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 company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker AI endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.Which solution will provide an explanation for the model's predictions?

A company uses a batching solution to process daily analytics. The company wants to provide near real-time updates, use open-source technology, and avoid managing or scaling infrastructure.Which solution will meet these requirements?

A company has a team of data scientists who use Amazon SageMaker AI notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker AI notebook instance.The company needs to centralize management of the team's permissions.Which solution will meet this requirement?

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?

An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.What should the ML engineer do to improve the training process?

A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon S3 to provide customers with a live conversational engine.The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.Which solution will meet these requirements with the LEAST operational overhead?

An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.Which solution will meet these requirements with the LEAST operational overhead?

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 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 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?

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 run an on-demand workflow to monitor bias drift for models that are deployed to real- time endpoints from the application.Which action will meet this requirement?

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 company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon S3 to provide customers with a live conversational engine.The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.Which solution will meet these requirements with the LEAST operational overhead?

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 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?

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 is experimenting with consecutive training jobs.How can the company MINIMIZE infrastructure startup times for these jobs?

An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.Which solution will meet these requirements?

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 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 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 company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ТВ 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 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?

An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.Which solution will meet these requirements in the MOST operationally efficient way?

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 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?

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 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?

What are the steps to integrate SageMaker Pipelines into an AWS CodePipeline workflow?1. Trigger the pipeline using source code changes in CodeCommit.2. Monitor pipeline execution and validate results.3. Integrate the SageMaker pipeline as a step in CodePipeline.4. Define the pipeline structure in SageMaker Pipelines.

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 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?

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 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 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 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 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, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.Which solution will meet these requirements?

Your team is evaluating a binary classification model trained for fraud detection. The model achieves 95% accuracy on the test set, but customer complaints suggest it frequently misses fraudulent transactions.What actions should you take to resolve this issue?(Choose three)

A company ingests sales transaction data using Amazon Data Firehose into Amazon OpenSearch Service. The Firehose buffer interval is set to 60 seconds.The company needs sub-second latency for a real-time OpenSearch dashboard.Which architectural change will meet this requirement?

A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.Which solution will meet these requirements in the LEAST amount of time?

Which type of machine learning problem would you solve using a regression model?

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 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 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?