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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q28-Q33):

NEW QUESTION # 28
During CPMAI Phase II of your project, your team is going through their data collection needs. One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?

A. Evaluate your data and use only what you have and build all models in house.
B. Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
C. Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre- trained models your company already owns and use those.
D. Have one team build all models in-house and the other team use pre-trained models and see which team' s models perform better.

Answer: B

Explanation:
The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project's domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.
=========

NEW QUESTION # 29
Using machine learning and other cognitive approaches to understand how to take past/existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior/interactions/data is a core part to which pattern(s) of AI?

A. Recognition Pattern
B. Goal Driven Systems
C. Predictive Analytics & Decision Support and Patterns and Anomalies
D. Predictive Analytics & Decision Support

Answer: D

Explanation:
The Predictive Analytics & Decision Support pattern is defined as using historical data (past behavior) to forecast future events and provide decision support for human or automated processes. This is distinct from the Patterns & Anomalies pattern, which focuses on detecting unusual deviations rather than forecasting expected outcomes.
A CPMAI Glossary self-test question states that Predictive Analytics "uses historical data to forecast future outcomes" .
Another glossary question defines predictive analytics as aiming "to use historical data to forecast future outcomes" .
=========

NEW QUESTION # 30
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)

A. Bias-variance tradeoff
B. The algorithm code
C. Supporting training data
D. Hyperparameter settings

Answer: B,C,D

Explanation:
Algorithm selection/code must be documented under the Select Modeling Technique task, where teams
"document the actual algorithm/modeling technique to be used" .
Supporting training data pipelines are a core artifact of Phase III: Data Cleansing, which mandates "create a reusable data pipeline to collect, ingest, and prepare data for training purposes" .
Hyperparameter settings are captured in the Hyperparameter Optimization task, where teams "list the final, optimized settings" used for model building .
The bias-variance tradeoff is a conceptual consideration during evaluation but is not a discrete artifact to include in the project deliverables.
=========

NEW QUESTION # 31
Your team is planning an AI-enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.
What stage of CPMAI is the team currently working on?

A. Phase II
B. Phase VI
C. Phase I
D. Phase IV
E. Phase III
F. Phase V

Answer: C

Explanation:
The AI Go/No Go assessment is part of Phase I: Business Understanding under the Cognitive Project Requirements generic task group. In Phase I, teams perform business-feasibility, data-feasibility, and execution-feasibility checks before proceeding with any AI work .
=========

NEW QUESTION # 32
Clean, well-labeled datasets used for machine learning are partitioned into three subsets: Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

A. Use the same data for all sets
B. Split by random subsampling
C. Split by patterned subsampling
D. Split by alphabetical order

Answer: B

Explanation:
CPMAI's glossary defines data splitting as "dividing a data set into subsets (e.g., training, validation, test) for model development and evaluation," typically achieved via random subsampling to ensure each subset is representative of the underlying distribution and to prevent sampling bias.

NEW QUESTION # 33
......

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Dan Lee Dan Lee

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