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 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 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 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 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 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 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 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 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 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 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?

What are common metrics used for evaluating classification models?​(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 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 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 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 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 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 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 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 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 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,

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 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 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.)