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Exam Code: MLA-C01 Vendor: Amazon Web Services Name: AWS Certified Machine Learning Engineer - Associate 241 Q&A with Explanation Last Update: Jun 10, 2026
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MLA-C01 Q&A's Detail

Exam Code:
MLA-C01
Total Questions:
241 Q&A's
Single Choice Questions:
215 Q&A's
Multiple Choice Questions:
7 Q&A's
Hotspot Questions:
19 Q&A's

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AWS Certified Machine Learning Engineer - Associate Questions and Answers

Question 1

A company uses a batching solution to process daily analytics. The company wants to provide near real-time updates, use open-source technology, and avoid managing or scaling infrastructure.

Which solution will meet these requirements?

Options:

A.

Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless clusters.

B.

Create Amazon MSK Provisioned clusters.

C.

Create Amazon Kinesis Data Streams with Application Auto Scaling.

D.

Create self-hosted Apache Flink applications on Amazon EC2.

Question 2

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.

How should the company deploy the model on Amazon SageMaker to meet these requirements?

Options:

A.

Use a multi-model serverless endpoint. Enable caching.

B.

Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.

C.

Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.

D.

Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.

Question 3

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?

Options:

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.

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