CPMAI Test Sample Questions | CPMAI Latest Exam Book

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Operationalize AI Solution17%- Manage change, adoption, and governance post-launch
- Establish monitoring, maintenance, and improvement processes
- Deploy AI systems into production
Manage AI Model Development and Evaluation16%- Oversee model design, training, and validation
- Address model drift, explainability, and limitations
- Monitor performance, accuracy, and reliability
Identify Data Needs26%- Define data requirements and sources
- Ensure data quality, privacy, security, and compliance
- Plan data collection, storage, and infrastructure
Support Responsible and Trustworthy AI Efforts15%- Manage bias, risk, compliance, and societal impact
- Establish ethical and governance frameworks
- Ensure fairness, transparency, accountability
Identify Business Needs and Solutions26%- Define requirements, scope, and success criteria
- Align AI initiatives with organizational strategy
- Evaluate feasibility and value of AI solutions

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q313-Q318):

NEW QUESTION # 313
A project manager is considering the feasibility of an AI solution for a project aimed at improving customer service response times. They need to decide whether the project requirements can be solved with existing technologies or whether AI is the best approach. Which principle do the project manager's activities represent?

Answer: A

Explanation:
Evaluating cognitive alternatives means determining whether the project requirements truly need AI-based cognitive capabilities or can be addressed using existing technologies, traditional automation, or non-AI approaches.


NEW QUESTION # 314
An IT services company is integrating an AI solution to automate its customer service functions.
The integration team is facing resistance from the customer's employees. Which action should the project manager perform to manage this risk?

Answer: A

Explanation:
Implementing a gradual phased rollout helps reduce employee resistance by allowing users to adapt incrementally, receive support, provide feedback, and build confidence in the AI solution before full transition.


NEW QUESTION # 315
In an aerospace manufacturing project, engineers are preparing data to train an AI system for predictive maintenance. They need to transform the data from multiple sensors and ensure it is consistent and accurate before building the model. What should the project manager do to handle the inconsistencies?

Answer: C

Explanation:
Identifying and reconciling conflicting data points directly addresses inconsistencies in sensor data before model training. This helps ensure the predictive maintenance model is trained on accurate, consistent, and reliable inputs.


NEW QUESTION # 316
An aerospace company is planning to integrate AI for predictive maintenance. The project team needs to identify which parts of the project require cognitive AI solutions. Which method should the project manager perform to meet the objective?

Answer: D

Explanation:
Conducting workshops with domain experts helps identify which predictive maintenance activities require cognitive AI capabilities, such as anomaly detection, failure prediction, and pattern recognition. Domain experts can map dependencies, clarify operational needs, and separate AI- suitable tasks from tasks that can be handled through traditional methods.


NEW QUESTION # 317
You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?

Answer: B

Explanation:
Generative Outpainting is a technique that extends the content of an existing image beyond its original boundaries, creating new image areas that blend seamlessly with the original.


NEW QUESTION # 318
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