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Amazon Web Services MLA-C01 Exam Dumps Questions and Answers

Exam Code: MLA-C01
Certification: AWS Certified Associate
Last Update: Jun 3, 2026
Questions: 241 With Expert Explanation
10  Customers Passed Amazon Web Services MLA-C01
85%  Average Success In Real Exam
94%  Questions came word by word from this dump
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AWS Certified Machine Learning Engineer - Associate Questions and Answers

Questions 1

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.

During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model ' s F1 score decreases significantly.

What could be the reason for the reduced F1 score?

Options:

A.

Concept drift occurred in the underlying customer data that was used for predictions.

B.

The model was not sufficiently complex to capture all the patterns in the original baseline data.

C.

The original baseline data had a data quality issue of missing values.

D.

Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Questions 2

A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.

The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.

Which solution will meet these requirements?

Options:

A.

Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

B.

Create a model group for each category. Move the existing models into these category model groups.

C.

Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.

D.

Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

Questions 3

An ML engineer is using Amazon Quick Suite (previously known as Amazon QuickSight) anomaly detection to detect very high or very low machine operating temperatures compared to normal. The ML engineer sets the Severity parameter to Low and above. The ML engineer sets the Direction parameter to All.

What effect will the ML engineer observe in the anomaly detection results if the ML engineer changes the Direction parameter to Lower than expected?

Options:

A.

Increased anomaly identification frequency and increased recall

B.

Decreased anomaly identification frequency and decreased recall

C.

Increased anomaly identification frequency and decreased recall

D.

Decreased anomaly identification frequency and increased recall