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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?

A. Ethical AI
B. Responsible AI
C. Transparent AI
D. Governed AI
E. Explainable AI

Answer: E

Explanation:
In CPMAI's Trustworthy AI requirements, the Explainable AI layer specifically covers "legal, compliance, and risk considerations [that] might require that the AI system used for decision-making ... provide some level of explainability for audit, root cause analysis, or other purposes." Providing applicants with the ability to contest hiring decisions depends on furnishing clear, human-understandable explanations of how and why the model arrived at its result-exactly the focus of the Required AI Explainability Considerations task.
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NEW QUESTION # 40
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 see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
B. Evaluate your data and use only what you have and build all models in house.
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: A

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.
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NEW QUESTION # 41
A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months.
What's wrong with this timeframe?

A. It's not accounting for potential project delays
B. It fits into a waterfall timeframe, but not an agile project timeframe
C. It's not accounting for data preparation timelines
D. It's underestimating the project timeline by 3 months

Answer: C

Explanation:
CPMAI's Phase III: Data Preparation is a distinct phase that encompasses data cleansing, augmentation, labeling, and pipeline construction. Because data engineering often accounts for the majority of AI project effort, omitting this phase from initial estimates leads to significant timeline underestimation. Project timelines must explicitly include Phase III activities to be realistic .
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NEW QUESTION # 42
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?

A. RPA is not the right solution to this problem
B. Augmented Intelligence
C. Attended bot
D. Unattended bot

Answer: C

Explanation:
In the CPMAI Glossary, attended bots are defined as "software automation tools that work alongside humans (typically in front-office roles) to assist with tasks and boost productivity." Call-center assistance is a classic front-office scenario requiring a bot that human agents can invoke interactively.
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NEW QUESTION # 43
The team is working to build a data preparation pipeline for the conversational chatbot project. Which phase of CPMAI is this done?

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

Answer: B

Explanation:
Phase III: Data Preparation focuses on constructing and documenting reusable data pipelines-including training and inference pipelines-alongside cleansing, augmentation, and labeling tasks to prepare data for modeling . This is where teams build the end-to-end data preparation workflows for AI solutions such as chatbots.

NEW QUESTION # 44
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