What is the primary purpose of documenting a model testing protocol before testing begins?

To provide a record of the model's training data sources for audit purposes

To assign ownership of the model to a specific team member

To ensure that test cases, environments, data, and pass/fail criteria are clearly defined and consistently applied

To list all possible edge cases that the model might encounter in production

A data scientist has completed a model and submitted it for peer review. The reviewer finds that the model code does not follow the team's style guide and lacks unit tests. What should the reviewer do?

Approve the model because the results are good

Ask another reviewer to provide a second opinion

Approve the model but note the issues in the review comments

Reject the review and require the data scientist to fix the code style and add unit tests

Which practice is an example of adhering to coding standards in an AI/ML project?

Hardcoding all hyperparameters in the script

Writing modular code with clear comments and documentation

Avoiding comments to keep the code short

Using a single large script for all model training steps

A team is developing a model for a medical diagnosis application. The testing protocol specifies a minimum recall of 0.95 and a minimum precision of 0.90. The current model achieves recall 0.96 and precision 0.88. The team has tried various thresholds and cannot meet both criteria simultaneously. What should the QA lead do?

Approve the model and document the precision shortfall as a known limitation

Approve the model because recall is above the threshold and precision is close

Change the precision threshold to 0.85 to accommodate the model

Reject the model and require the team to improve precision without sacrificing recall

What is the goal of technical validation of a model design?

To write the model's user manual

To select the model's deployment platform

To calculate the cost of training the model

To verify that the design meets requirements and follows best practices

PMICertified Professional in Managing AI (PMI-CPMAI)

Domain 4Objective 2

QC (e.g., Configuration Management, Model Performance) PMI-CPMAI Practice Questions (Page 6)

Part of the Manage AI Model Development and Evaluation domain, which accounts for 16% of the PMI-CPMAI exam. PMI does not publish an official question count, but from its 160-minute exam (~65–105 total, ~10–17 in this domain), expect 2–3 from this objective — we provide 33 practice questions to prepare you well beyond it. (estimate)

33questions here

7free pages

9concepts

16%of the exam

9 Concepts in This Objective

Model Testing Protocols
QA Procedures for AI/ML
Configuration Management Basics
Model Versioning and Parameters
Performance Metrics Monitoring
Peer Review Process
Technical Validation of Models
Coding Standards Adherence
Best Practices in Model Development

Questions 26–30

26foundation · easy
What is the primary purpose of documenting a model testing protocol before testing begins?
Select an answer first
27application · medium
A data scientist has completed a model and submitted it for peer review. The reviewer finds that the model code does not follow the team's style guide and lacks unit tests. What should the reviewer do?
Select an answer first
28foundation · easy
Which practice is an example of adhering to coding standards in an AI/ML project?
Select an answer first
29expert · hard
A team is developing a model for a medical diagnosis application. The testing protocol specifies a minimum recall of 0.95 and a minimum precision of 0.90. The current model achieves recall 0.96 and precision 0.88. The team has tried various thresholds and cannot meet both criteria simultaneously. What should the QA lead do?
Select an answer first
30foundation · easy
What is the goal of technical validation of a model design?
Select an answer first

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