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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q279-Q284):

NEW QUESTION # 279
A sponsor asks the project manager to commit to a fixed delivery date and a specific model accuracy figure at project initiation.
How should the project manager respond?

A. Explain that achievable accuracy cannot be known before data and modeling work, and propose phase-gate commitments
B. Decline to provide estimates until the model is trained
C. Commit to the date and treat accuracy as a stretch goal
D. Commit to both and manage variance through change control

Answer: A

Explanation:
Achievable model performance depends on data quality and characteristics that are unknown at initiation. Committing to phase-level outcomes with go/no-go decision points gives the sponsor control and visibility without requiring an unsupportable guarantee.

NEW QUESTION # 280
When looking to implement AI to help break the Digital Transformation logjam, it's important to:

A. Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non digital processes
B. Invest in technology solutions to solve the problem
C. Start with basic automation and introduce AI only as needed
D. Have the right culture for AI

Answer: D

Explanation:
Having the right culture for AI is crucial to successfully adopt and integrate AI technologies, fostering openness, collaboration, and innovation that drive digital transformation forward.

NEW QUESTION # 281
You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results.
What's your best course of action?

A. Try to get additional information from project lead to see how many examples per dimension are needed
B. Try to improve the quality of your data through more preparation
C. Try to get additional data - at least 5 training examples for each dimension in the representation
D. Keep going as planned and the problem will eventually correct itself

Answer: C

NEW QUESTION # 282
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?

A. Identify and reconcile conflicting data points.
B. Enhance the current data with additional sources.
C. Implement a validation protocol for sensor data.
D. Use data augmentation techniques to fill the gaps.

Answer: A

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 # 283
You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Choose all that apply.)

A. Strong math skills, especially in calculus and statistics
B. Understanding of algorithms
C. Critical thinking skills
D. Prompt engineering skills
E. Automation skills, especially around creating RPA bots
F. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets

Answer: A,B,C,D,F

Explanation:
A data scientist should have prompt engineering skills, expertise with data manipulation tools, strong critical thinking, solid understanding of algorithms, and strong math skills in calculus and statistics to effectively build and interpret AI models.

NEW QUESTION # 284
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