A company is using SageMaker Clarify to evaluate a hiring model for potential bias. The model predicts whether a candidate should be interviewed. The dataset includes a 'gender' feature. The data scientist runs a Clarify analysis and obtains a 'Difference in Positive Proportions in Predicted Labels' (DPPL) metric. The DPPL value is 0.15, indicating that the predicted positive rate for female candidates is 15 percentage points lower than for male candidates. What is the most appropriate interpretation?

The model shows a disparity in predicted outcomes between groups, which warrants further investigation into the data and model.

The model is definitively biased against female candidates and must be retrained.

The model is biased against male candidates because the DPPL is positive.

The model is unbiased because the DPPL value is below the typical threshold of 0.2.

AWSCertified Machine Learning Engineer - Associate

Domain 2Objective 3

Task 2.3: Analyze Model Performance MLA-C01 Practice Questions (Page 4)

Part of the Content Domain 2: ML Model Development domain, which accounts for 26% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~13–22 in this domain), expect 4–7 from this objective — we provide 39 practice questions to prepare you well beyond it. (estimate)

39questions here

8free pages

15concepts

26%of the exam

Questions 16–20

16foundation · easy
Which regression metric is expressed in the same units as the target variable and gives the average absolute magnitude of errors?
Select an answer first
17application · medium
A company is using SageMaker Clarify to evaluate a hiring model for potential bias. The model predicts whether a candidate should be interviewed. The dataset includes a 'gender' feature. The data scientist runs a Clarify analysis and obtains a 'Difference in Positive Proportions in Predicted Labels' (DPPL) metric. The DPPL value is 0.15, indicating that the predicted positive rate for female candidates is 15 percentage points lower than for male candidates. What is the most appropriate interpretation?
Select an answer first
18foundation · easy
Which AWS service is used to track and manage machine learning experiments, including parameters, metrics, and artifacts?
Select an answer first
19foundation · easy
Which SageMaker Clarify feature provides feature attribution values to explain individual predictions?
Select an answer first
20foundation · easy
In a confusion matrix for a binary classification problem, which cell represents the number of negative instances that were incorrectly predicted as positive?
Select an answer first

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Task 2.3: Analyze Model Performance MLA-C01 Practice Questions (Page 4)

Questions 16–20

Which regression metric is expressed in the same units as the target variable and gives the average absolute magnitude of errors?

Which AWS service is used to track and manage machine learning experiments, including parameters, metrics, and artifacts?

Which SageMaker Clarify feature provides feature attribution values to explain individual predictions?

In a confusion matrix for a binary classification problem, which cell represents the number of negative instances that were incorrectly predicted as positive?

Keep studying — Content Domain 2: ML Model Development