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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Identify Data Needs	26%	- Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources
Topic 2: Manage AI Model Development and Evaluation	16%	- Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations
Topic 3: Support Responsible and Trustworthy AI Efforts	15%	- Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks
Topic 4: Operationalize AI Solution	17%	- Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production
Topic 5: Identify Business Needs and Solutions	26%	- Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q221-Q226):

NEW QUESTION # 221
You have an Anomaly Detection project you're working on and you need a simple approach of clustering data into classified groups. Which algorithm is the best choice given this situation?

A. Hidden Markov Model
B. Decision Tree
C. K-Means Clustering
D. Neural Network

Answer: C

Explanation:
K-Means clustering is a straightforward and widely used algorithm for grouping data into clusters, suitable for anomaly detection tasks that require classification into groups.

NEW QUESTION # 222
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:

A. Provide a means to have a different team on the project
B. Provide a means to have contestability of the algorithm selected
C. Provide a means to interpret AI results so that cause and effect can be represented.
D. Provide a means to reverse-engineer the algorithm to inspect its performance

Answer: C

NEW QUESTION # 223
Multiple teams are developing AI solutions independently, resulting in inconsistent ethical practices and governance standards. Leadership wants to ensure alignment across all projects.
What is the best action for the project manager to recommend?

A. Allow teams full autonomy
B. Increase model complexity
C. Establish an AI governance framework and policies
D. Focus only on technical performance

Answer: C

Explanation:
Governance frameworks ensure consistent ethical and operational standards.

NEW QUESTION # 224
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:

A. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
B. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
C. Make sure all senior leadership is data literate, understands the Vs of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.
D. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.

Answer: C

NEW QUESTION # 225
An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution. At what point in the project should the requirements be approached?

A. As optional guidelines based on project scope
B. As part of the final testing phase
C. As part of the business understanding phase
D. As part of the data preparation phase

Answer: C

Explanation:
Compliance with data regulations and explainability standards should be addressed during the business understanding phase so they become formal project requirements from the start. This ensures the AI solution is planned, designed, validated, and operationalized in alignment with legal, ethical, and stakeholder expectations.

NEW QUESTION # 226
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