Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
Splunk
ServiceNow
All Vendors
  2. Home
  3. Exams
  4. Amazon
  5. AWS Certified Machine Learning Engineer - Associate MLA-C01

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

Updated On: 7-Feb-2026
Page 2 of 47
PDF with 230 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.






Post your Comments and Discuss Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 exam prep with other Community members:

Join the AWS Certified Machine Learning Engineer - Associate MLA-C01 Discussion