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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Operationalize AI Solution17%- Manage change, adoption, and governance post-launch
- Deploy AI systems into production
- Establish monitoring, maintenance, and improvement processes
Topic 2: Manage AI Model Development and Evaluation16%- Monitor performance, accuracy, and reliability
- Address model drift, explainability, and limitations
- Oversee model design, training, and validation
Topic 3: Identify Business Needs and Solutions26%- Align AI initiatives with organizational strategy
- Evaluate feasibility and value of AI solutions
- Define requirements, scope, and success criteria
Topic 4: Support Responsible and Trustworthy AI Efforts15%- Establish ethical and governance frameworks
- Manage bias, risk, compliance, and societal impact
- Ensure fairness, transparency, accountability
Topic 5: Identify Data Needs26%- Ensure data quality, privacy, security, and compliance
- Define data requirements and sources
- Plan data collection, storage, and infrastructure

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q136-Q141):

NEW QUESTION # 136
In an IT services firm, the AI project team is tasked with developing a virtual assistant to support customer service operations. The assistant must integrate seamlessly with existing customer relationship management (CRM) systems and handle a variety of customer queries.
Which necessary initial task should the project manager take?

Answer: D

Explanation:
A comprehensive data audit should be performed first to identify what customer and CRM data is available, assess its quality and accessibility, and determine integration constraints. This establishes whether the virtual assistant has the reliable data foundation needed to handle customer queries effectively.


NEW QUESTION # 137
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?

Answer: C


NEW QUESTION # 138
A project team is preparing to move to the next phase of implementation with an AI system.
However, they are unsure about the system's transparency requirements. Which next step should the project manager prioritize?

Answer: A

Explanation:
Reviewing existing transparency standards and guidelines should be prioritized because the team must first understand the applicable expectations, regulatory requirements, and accepted practices before defining documentation, reporting, or stakeholder processes for the next phase.


NEW QUESTION # 139
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: C

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 # 140
An aerospace company is evaluating whether their sensor data meets the requirements for an AI- based predictive maintenance system. The project team needs to ensure that the data's accuracy, resolution, and timeliness are adequate to predict equipment failures. Which method addresses the requirements?

Answer: B

Explanation:
Performing a data quality assessment focused on precision and latency directly verifies whether the sensor data is accurate enough, captured at the right resolution, and available quickly enough to support reliable predictive maintenance decisions.


NEW QUESTION # 141
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