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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: Operationalize AI Solution	17%	- Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes
Topic 3: 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
Topic 4: Identify Data Needs	26%	- Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure
Topic 5: Support Responsible and Trustworthy AI Efforts	15%	- Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q254-Q259):

NEW QUESTION # 254
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. Attended bot
C. Unattended bot
D. Augmented Intelligence

Answer: B

NEW QUESTION # 255
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 improve the quality of your data through more preparation
B. Keep going as planned and the problem will eventually correct itself
C. Try to get additional information from project lead to see how many examples per dimension are needed
D. Try to get additional data - at least 5 training examples for each dimension in the representation

Answer: D

Explanation:
Obtaining sufficient training examples for each dimension helps ensure the model can learn reliable patterns and improve prediction accuracy in high-dimensional datasets.

NEW QUESTION # 256
In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data? As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?

A. Contract with Third Party Data Labeling Firms
B. Hire a Contractor Workforce
C. Get your Users to Do it
D. Find a source of already labeled data

Answer: C

Explanation:
Relying on users to label data can lead to inconsistent, low-quality labels and is generally inefficient, making it less beneficial compared to professional or curated labeling approaches.

NEW QUESTION # 257
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?

A. When in doubt, go with the patterns and anomalies pattern as all AI projects are about pattern matching.
B. Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
C. When in doubt, don't apply any pattern of AI.
D. Apply every pattern to the project.

Answer: B

NEW QUESTION # 258
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?

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

Answer: C

NEW QUESTION # 259
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