Amazon MLA-C01 Exam Practice Test Instant Access

Question 1

An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.

Which file format will meet these requirements?

ACSV files compressed with Snappy

BJSON objects in JSONL format

CJSON files compressed with gzip

DApache Parquet files

Answer : D

Question 2

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.

Answer : C

Question 3

An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur.

Which solution will meet these requirements?

ADeploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.

BDeploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.

CDeploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.

DDeploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.

Answer : D

Question 4

An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.

The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.

Which solution will meet these requirements?

ACreate AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.

BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.

CDeploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.

DDeploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.

Answer : D

Question 5

A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.

Which solution will meet this requirement?

AStore the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.

BStore the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.

CStore the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.

DStore the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.

Answer : A

Question 6

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.

Answer : A

Question 7

A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.

Which solution will meet these requirements?

AUse Amazon Made to categorize the sensitive data.

BPrepare the data by using AWS Glue DataBrew.

CRun an AWS Batch job to change the sensitive data to random values.

DRun an Amazon EMR job to change the sensitive data to random values.

Answer : B

Amazon MLA-C01 AWS Certified Machine Learning Engineer - Associate Exam Practice Test