MLA-C01 Free Practice Test

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 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?

1. A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
2. B. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
3. C. Use AWS Glue Data Quality to monitor bias.
4. D. Use SageMaker notebooks to compare the bias.

Correct Answer: A

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?

1. A. Use zero buffering in the Firehose strea
2. B. Tune the batch size that is used in the PutRecordBatch operation.
3. C. Replace the Firehose stream with an AWS DataSync tas
4. D. Configure the task with enhanced fan-out consumers.
5. E. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
6. F. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.

Correct Answer: A

A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?

1. A. Log the metrics from the Lambda function to AWS CloudTrai
2. B. Configure a CloudTrail trail to send the email message.
3. C. Log the metrics from the Lambda function to Amazon CloudFron
4. D. Configure an Amazon CloudWatch alarm to send the email message.
5. E. Log the metrics from the Lambda function to Amazon CloudWatc
6. F. Configure a CloudWatch alarm to send the email message.
7. G. Log the metrics from the Lambda function to Amazon CloudWatc
8. H. Configure an Amazon CloudFront rule to send the email message.

Correct Answer: D

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?

1. A. Configure ECR cross-account replication for each existing ECR repositor
2. B. Ensure that each model is visible in each AWS account.
3. C. Create a new AWS account with a new ECR repository as the central catalo
4. D. Configure ECR cross-account replication between the initial ECR repositories and the central catalog.
5. E. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon EC
6. F. Create a new AWS accoun
7. G. In the new account, use the SageMaker Model Registry as the central catalo
8. H. Attach a cross-account resource policy to each model group in the initial AWS accounts.
9. I. Use an AWS Glue Data Catalog to store the model
10. J. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalo
11. K. Configure cross- account access to the Data Catalog.

Correct Answer: C

A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

1. A. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
2. B. Create an AWS Lambda function that scans the S3 bucke
3. C. Program the Lambda function to initiate the pipeline when new data is uploaded.
4. D. Create an Amazon EventBridge rule that has an event pattern that matches the S3 uploa
5. E. Configure the pipeline as the target of the rule.
6. F. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.A company wants to develop an ML model by using tabular data from its customer
7. G. The data contains meaningful ordered features with sensitive information that should not be discarde
8. H. 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?
9. I. Use Amazon Made to categorize the sensitive data.
10. J. Prepare the data by using AWS Glue DataBrew.
11. K. Run an AWS Batch job to change the sensitive data to random values.
12. L. Run an Amazon EMR job to change the sensitive data to random values.

Correct Answer: B