MLA-C01 sample questions

Question 1

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 use the central model registry to manage different versions of models in the application.Which action will meet this requirement with the LEAST operational overhead?

Accepted Answer

Use the SageMaker Model Registry and model groups to catalog the models.

Answer

Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.

Answer

Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.

Answer

Use the SageMaker Model Registry and unique tags for each model version.

Question 2

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 is experimenting with consecutive training jobs.How can the company MINIMIZE infrastructure startup times for these jobs?

Accepted Answer

Use SageMaker managed warm pools.

Answer

Use Managed Spot Training.

Answer

Use SageMaker Training Compiler.

Answer

Use the SageMaker distributed data parallelism (SMDDP) library.

Question 3

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 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?

Accepted Answer

Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

Answer

Use SageMaker Experiments to facilitate the approval process during model registration.

Answer

Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.

Answer

Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.

Question 4

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?

Accepted Answer

Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.

Answer

Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.

Answer

Use AWS Glue Data Quality to monitor bias.

Answer

Use SageMaker notebooks to compare the bias.

Question 5

A financial services company is building a real-time fraud detection system. The system ingests transaction data via Amazon Kinesis Data Streams. A Machine Learning Engineer needs to perform feature engineering on this streaming data (such as calculating rolling averages of transaction amounts over the last 10 minutes) before passing the features to a SageMaker endpoint for inference. Which approach offers the low-latency feature calculation required?

Accepted Answer

Use Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) to compute the rolling averages on the stream and write the results to Amazon SageMaker Feature Store (Online Store).

Answer

Use Amazon Kinesis Data Firehose to buffer the data to Amazon S3, then trigger an AWS Glue ETL job to calculate rolling averages.

Answer

Use Amazon SageMaker Data Wrangler to connect directly to the Kinesis Data Stream and apply the rolling window transformation.

Answer

Use AWS Lambda to process each record individually and store the state in Amazon S3 standard storage.

Question 6

A Machine Learning Engineer is preparing a large dataset (50 TB) stored in Amazon S3 for training a computer vision model. The training job will run on a cluster of Amazon EC2 p4d.24xlarge instances using Amazon SageMaker. The dataset consists of millions of small image files. The engineer observes that the training job initialization is taking a long time due to S3 API latency when listing and downloading objects. Which storage configuration will MAXIMIZE data loading performance?

Accepted Answer

Configure the training job to use Amazon FSx for Lustre linked to the S3 bucket.

Answer

Use Amazon S3 standard storage and enable S3 Transfer Acceleration.

Answer

Copy the data to Amazon EBS General Purpose SSD (gp3) volumes attached to the training instances.

Answer

Use SageMaker Pipe Mode to stream the data directly from S3.

Question 7

A retail company wants to train a customer churn prediction model. The dataset contains sensitive Personally Identifiable Information (PII) including customer names and email addresses. The company's security policy requires that all PII be redacted before the data is used for model training. Which solution provides the MOST automated and serverless way to identify and redact this PII in Amazon S3?

Accepted Answer

Use AWS Glue DataBrew with a PII detection transformation step to identify and mask the sensitive columns.

Answer

Use Amazon Macie to identify PII, then trigger a Lambda function to redact the findings.

Answer

Use Amazon SageMaker Ground Truth to manually label the PII fields and then write a script to remove them.

Answer

Use Amazon Inspector to scan the S3 bucket for vulnerabilities and PII.

Question 8

An ML Engineer is setting up a feature engineering pipeline. The requirement is to have a centralized repository where features can be stored, discovered, and shared across different teams. The solution must support both low-latency retrieval for real-time inference and high-throughput retrieval for batch training. Which AWS service component should be used?

Accepted Answer

Amazon SageMaker Feature Store

Answer

Amazon S3 with Intelligent-Tiering

Answer

Amazon DynamoDB with DAX

Answer

AWS Glue Data Catalog

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