Expert Answers to Snowflake Exam MLA-C01 Questions

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SnowPro Advanced: MLOps Engineer SnowPro Advanced: MLOps Engineer Certification Exam

SnowPro Advanced: MLOps Engineer Certification Exam

Last Update Nov 12, 2026
Total Questions : 0

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Questions 2

A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model ' s hyperparameters to minimize the loss function on the validation dataset.

Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

Options:

Hyperbaric!

Grid search

Bayesian optimization

Random search

Discussion 0

Questions 3

A company is using Amazon SageMaker AI to build an ML model to predict customer behavior. The company needs to explain the bias in the model to an auditor. The explanation must focus on demographic data of the customers.

Which solution will meet these requirements?

Options:

Use SageMaker Clarify to generate a bias report. Send the report to the auditor.

Use AWS Glue DataBrew to create a job to detect drift in the model ' s data quality. Send the job output to the auditor.

Use Amazon QuickSight integration with SageMaker AI to generate a bias report. Send the report to the auditor.

Use Amazon CloudWatch metrics from the SageMaker AI namespace to create a bias dashboard. Share the dashboard with the auditor.

Discussion 0

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Questions 4

A company must install a custom script on any newly created Amazon SageMaker AI notebook instances.

Which solution will meet this requirement with the LEAST operational overhead?

Options:

Create a lifecycle configuration script to install the custom script when a new SageMaker AI notebook is created. Attach the lifecycle configuration to every new SageMaker AI notebook as part of the creation steps.

Create a custom Amazon Elastic Container Registry (Amazon ECR) image that contains the custom script. Push the ECR image to a Docker registry. Attach the Docker image to a SageMaker Studio domain. Select the kernel to run as part of the SageMaker AI notebook.

Create a custom package index repository. Use AWS CodeArtifact to manage the installation of the custom script. Set up AWS PrivateLink endpoints to connect CodeArtifact to the SageMaker AI instance. Install the script.

Store the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker AI notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker AI notebook is initialized.

Discussion 0

Questions 5

An ML engineer must choose the appropriate Amazon SageMaker algorithm to solve specific AI problems.

Select the correct SageMaker built-in algorithm from the following list for each use case. Each algorithm should be selected one time.

• Random Cut Forest (RCF) algorithm

• Semantic segmentation algorithm

• Sequence-to-Sequence (seq2seq) algorithm

Questions 5