Amazon MLA-C01 Practice Test - Questions Answers, Page 7

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 needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.

The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.

Which solution will provide the ML engineers with the appropriate access?

A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.

Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.

Which solution will meet these requirements?

A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker 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?

An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.

What should the ML engineer do to MINIMIZE the communication overhead between the instances?

A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.

Which solution will meet these requirements with the LEAST effort?

A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.

Which solution will meet these requirements in the LEAST amount of time?

HOTSPOT

A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.

Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)

* An S3 event notification invokes the pipeline when new data is uploaded.

* S3 Lifecycle rule invokes the pipeline when new data is uploaded.

* SageMaker retrains the model by using the data in the S3 bucket.

* The pipeline deploys the model to a SageMaker endpoint.

* The pipeline deploys the model to SageMaker Model Registry.

HOTSPOT

An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).

Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)

* Embedding

* Retrieval Augmented Generation (RAG)

* Temperature

* Token