Amazon MLA-C01 Quiz 1 Topic 1 Questions 1-5

Question: 1

A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.

Which solution will set up the required online validation with the LEAST operational overhead?

AUse production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.

BUse production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.

CCreate a new SageMaker endpoint. Use production variants to add the new model to the new endpoint. Monitor the number of invocations by using Amazon CloudWatch.

DConfigure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint. Monitor the number of invocations by using AWS CloudTrail.

Show Answer

Question: 2

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?

AUse Amazon Mechanical Turk jobs to detect duplicates.

BUse Amazon QuickSight ML Insights to build a custom deduplication model.

CUse Amazon SageMaker Data Wrangler to pre-process and detect duplicates.

DUse the AWS Glue FindMatches transform to detect duplicates.

Show Answer

Question: 3

A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.

Which solution will meet these requirements?

AConfigure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.

BCreate a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog.

CUse the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.

DUse an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.

Show Answer

Question: 4

A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.

An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.

Which solution will meet these requirements?

AUse SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.

BUse SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.

CUse SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

DKeep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

Show Answer

Question: 5

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.

How should the company deploy the model on Amazon SageMaker to meet these requirements?

AUse a multi-model serverless endpoint. Enable caching.

BUse an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.

CUse a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.

DUse a serverless inference endpoint. Set the MaxConcurrency parameter to 1.

Show Answer

Amazon MLA-C01 Quiz:1 Topic:1 Questions:1-5