MLA-C01 Exam Details

  • Exam Code
    :MLA-C01
  • Exam Name
    :AWS Certified Machine Learning Engineer - Associate (MLA-C01)
  • Certification
    :Amazon Certifications
  • Vendor
    :Amazon
  • Total Questions
    :124 Q&As
  • Last Updated
    :Jul 09, 2026

Amazon MLA-C01 Online Questions & Answers

  • Question 51:

    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?

    A. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
    B. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
    C. Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
    D. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.

  • Question 52:

    A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish. Which solution will meet these requirements in the LEAST amount of time?

    A. Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
    B. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
    C. Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
    D. Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.

  • Question 53:

    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?

    A. CSV files compressed with Snappy
    B. JSON objects in JSONL format
    C. JSON files compressed with gzip
    D. Apache Parquet files

  • Question 54:

    A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic

    search of text files. The company has already migrated the text repository to an Amazon S3 bucket.

    Which solution will meet these requirements?

    A. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
    B. Use a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
    C. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.
    D. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.

  • Question 55:

    A company requires an AWS solution that can automatically generate versions of machine learning models as they are created. Which solution would effectively fulfill this requirement?

    A. Amazon Elastic Container Registry (Amazon ECR)
    B. Model packages from Amazon SageMaker Marketplace
    C. Amazon SageMaker ML Lineage Tracking
    D. Amazon SageMaker Model Registry

  • Question 56:

    An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production, using the InvokeEndpoint API operation. However, the model's latency in production is higher than the baseline latency observed in the test environment. The engineer suspects that the increased latency may be due to model startup time.

    What steps should the ML engineer take to confirm or deny this hypothesis?

    A. Schedule a SageMaker Model Monitor job. Observe metrics about model quality.
    B. Schedule a SageMaker Model Monitor job with Amazon CloudWatch metrics enabled.
    C. Enable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace.
    D. Enable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace.

  • Question 57:

    A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict. The company needs to use the dataset in a solution to determine if a model can predict the target variable. Which solution will provide this information with the LEAST development effort?

    A. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
    B. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
    C. Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
    D. Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.

  • Question 58:

    A company currently utilizes 10 Reserved Instances of accelerated instance types to host the existing version of an ML model on an Amazon SageMaker real-time inference endpoint. An ML engineer needs to deploy a new version of the model while ensuring that both versions are served using the original 10 instances. Additionally, one extra Reserved Instance must be available for the deployment process. The transition between model versions must occur seamlessly, without any downtime or service interruptions.

    Which solution would effectively fulfill these requirements?

    A. Configure a blue/green deployment with all-at-once traffic shifting.
    B. Configure a blue/green deployment with canary traffic shifting and a size of 10%.
    C. Configure a shadow test with a traffic sampling percentage of 10%.
    D. Configure a rolling deployment with a rolling batch size of 1.

  • Question 59:

    A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account. An ML engineer needs to set up an ML pipeline in the primary account to access the S3

    bucket in the secondary account. The solution must not require public IPv4 addresses.

    Which solution will meet these requirements?

    A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.
    B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0.
    C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
    D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift.

  • Question 60:

    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?

    A. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
    B. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
    C. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
    D. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.

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