AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Questions and Answers (Page: 1)

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?

Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
Use the SageMaker Model Registry and model groups to catalog the models.
Use the SageMaker Model Registry and unique tags for each model version.

Answer(s): C

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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?

Use Managed Spot Training.
Use SageMaker managed warm pools.
Use SageMaker Training Compiler.
Use the SageMaker distributed data parallelism (SMDDP) library.

Answer(s): B

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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?

Use SageMaker Experiments to facilitate the approval process during model registration.
Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

Answer(s): D

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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?

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

Answer(s): A

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QUESTION: 7

HOTSPOT (Drag and Drop is not supported)
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
• An S3 event notification invokes the pipeline when new data is uploaded.
• S3 Lifecycle rule invokes the pipeline when new data is uploaded.
• SageMaker retrains the model by using the data in the S3 bucket.
• The pipeline deploys the model to a SageMaker endpoint.
• The pipeline deploys the model to SageMaker Model Registry.

See Explanation section for answer.

Answer(s): A

Explanation:

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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam (page: 1)
Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01
Updated on: 07-Nov-2025

Access free AWS Certified Machine Learning Engineer - Associate MLA-C01 actual exam questions, answers, explanations and user discussions.

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QUESTION: 2

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QUESTION: 4

QUESTION: 7

Explanation:

Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam (page: 1) Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Updated on: 07-Nov-2025

Access free AWS Certified Machine Learning Engineer - Associate MLA-C01 actual exam questions, answers, explanations and user discussions.

QUESTION: 1

QUESTION: 2

QUESTION: 3

QUESTION: 4

QUESTION: 7

Explanation:

Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam (page: 1)
Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01
Updated on: 07-Nov-2025