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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Actual Questions
AWS Certified Machine Learning Engineer - Associate MLA-C01 (Page 2 )

Updated On: 18-Jul-2026
Page 2 of 33
PDF with 270 Questions

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?

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

Answer(s): C

Explanation:

The SageMaker Model Registry is specifically designed to manage the lifecycle of machine learning models, including versioning, deployment, and monitoring. By using model groups, the registry allows cataloging and organizing models based on different criteria, such as use case or project. This approach minimizes operational overhead by providing an integrated solution within SageMaker for model versioning and management.



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?

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

Answer(s): B

Explanation:

SageMaker managed warm pools help minimize infrastructure startup times for training jobs by keeping instances warm and ready to be reused for subsequent jobs. This significantly reduces the initialization time that is typically required when starting new training jobs, making it ideal for scenarios involving consecutive training jobs. This approach ensures efficient utilization of resources with minimal delays between jobs.



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?

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

Answer(s): D

Explanation:

SageMaker Pipelines is a purpose-built feature for creating, automating, and managing ML workflows. It integrates seamlessly with the SageMaker Model Registry, which supports setting approval statuses for model versions. By using the AWS SDK to update the model's status to "Approved," the company can implement a manual approval process that ensures only approved models are deployed to production. This approach is efficient and aligns well with the requirement for manual approvals while leveraging SageMaker's built-in capabilities.



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

Answer(s): A

Explanation:

SageMaker Clarify is designed to detect and monitor bias in ML models and datasets. By running a Clarify job, the company can analyze the deployed model for bias drift. Configuring the application to invoke an AWS Lambda function to trigger the SageMaker Clarify job allows for on-demand and automated monitoring of bias drift in real-time endpoints. This solution ensures operational efficiency and meets the requirement for secure and automated bias monitoring.



HOTSPOT

A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.

Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)

· Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
· Store the resulting data back in Amazon S3.
· Use Amazon Athena to infer the schemas and available columns. · Use AWS Glue crawlers to infer the schemas and available columns. · Use AWS Glue DataBrew for data cleaning and feature engineering.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




The correct steps, in order, are:
1. Use AWS Glue crawlers to infer the schemas and available columns.
- AWS Glue crawlers can automatically scan the .csv files in Amazon S3, detect the schema, and catalog the data for further processing.
2. Use AWS Glue DataBrew for data cleaning and feature engineering.
- AWS Glue DataBrew provides tools for cleaning, transforming, and preparing the data for ML tasks.
3. Store the resulting data back in Amazon S3.
- After cleaning and preparing the data, the resulting dataset can be stored back in Amazon S3 for training ML models.



HOTSPOT

An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.

Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)

· Access the store to build datasets for training.
· Create a feature group.
· Ingest the records.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




The correct steps, in order, are:
4. Create a feature group.
A feature group defines the schema and structure for the features, serving as a container for storing and



organizing features.
5. Ingest the records.
Populate the feature group by ingesting data records, which include the features and their associated values.
6. Access the store to build datasets for training.
Retrieve features from the Feature Store to construct datasets for model training.



HOTSPOT

A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (CI/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.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




The correct steps, in order, are:
7. An S3 event notification invokes the pipeline when new data is uploaded. Set up an S3 event notification to trigger the pipeline when new training data is added to the S3 bucket.



8. SageMaker retrains the model by using the data in the S3 bucket. The pipeline should include a step to retrain the ML model using the new data in the S3 bucket.



9. The pipeline deploys the model to a SageMaker endpoint. After retraining, the pipeline deploys the updated model to a SageMaker endpoint for inference.



HOTSPOT

An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).

Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all.

· Embedding
· Retrieval Augmented Generation (RAG)
· Temperature
· Token

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




The correct terms for each description are:
10. Text representation of basic units of data processed by LLMs - Token Tokens are the basic units of text (such as words or subwords) that LLMs process.



11. High-dimensional vectors that contain the semantic meaning of text - Embedding Embeddings are numerical representations of text in high-dimensional space, capturing semantic



meaning.
12. Enrichment of information from additional data sources to improve a generated response - Retrieval Augmented Generation (RAG)
RAG involves retrieving relevant information from external data sources to enhance the quality of generated



responses.



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