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Amazon Web Services MLA-C01 Exam Dumps Questions and Answers

Exam Code: MLA-C01
Certification: AWS Certified Associate
Last Update: Mar 1, 2026
Questions: 207 With Expert Explanation
10  Customers Passed Amazon Web Services MLA-C01
91%  Average Success In Real Exam
89%  Questions came word by word from this dump
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AWS Certified Machine Learning Engineer - Associate Questions and Answers

Questions 1

An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur.

Which solution will meet these requirements?

Options:

A.

Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.

B.

Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.

C.

Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.

D.

Deploy the models by using Amazon SageMaker AI batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.

Questions 2

An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.

Which solution will meet this requirement?

Options:

A.

Apply statistics from a well-known dataset to normalize the production samples.

B.

Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.

C.

Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.

D.

Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.

Questions 3

A company is developing a generative AI conversational interface to assist customers with payments. The company wants to use an ML solution to detect customer intent. The company does not have training data to train a model.

Which solution will meet these requirements?

Options:

A.

Fine-tune a sequence-to-sequence (seq2seq) algorithm in Amazon SageMaker JumpStart.

B.

Use an LLM from Amazon Bedrock with zero-shot learning.

C.

Use the Amazon Comprehend DetectEntities API.

D.

Run an LLM from Amazon Bedrock on Amazon EC2 instances.