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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q285-Q290):

NEW QUESTION # 285
A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
Which action should the project manager take?

A. Do not move forward until access is given to all the necessary data.
B. Move forward while anticipating data access is given when needed. An iterative approach provides the ability to return to steps as needed later on.
C. Move forward cautiously with the understanding that there may be a need for a pause mid-project.
D. Move forward in order to remain on schedule with the project.

Answer: B

Explanation:
An iterative approach allows the team to begin with the available data while revisiting earlier project activities as additional data becomes accessible. This supports progress without assuming that every data dependency must be resolved before the forecasting project can start.

NEW QUESTION # 286
During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates. Which action will identify the cause of the performance decline?

A. Increasing the amount of regularization to prevent overfitting
B. Checking for issues in the data preprocessing pipeline that may have introduced noise
C. Reviewing recent changes made to the model's architecture and parameters
D. Analyzing the distribution of real world data for potential shifts

Answer: D

Explanation:
Analyzing the distribution of real-world data helps determine whether data drift has occurred. A shift between the data the model was trained on and the data it now receives can reduce accuracy and increase error rates, even when the model previously performed well.

NEW QUESTION # 287
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A. Implement a new data privacy policy
B. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
C. Store the data in a data warehouse
D. Use noise reduction techniques to reduce all forms of data noise

Answer: B

Explanation:
Data anonymization protects individuals' privacy by removing or obfuscating personally identifiable information when such data is not necessary for model training.

NEW QUESTION # 288
An AI model trained on historical transaction data performs well initially. Over time, customer behavior changes due to market conditions, causing declining prediction accuracy. The team observes a mismatch between training and current data distributions. What is this issue called?

A. Concept drift
B. Underfitting
C. Overfitting
D. Data leakage

Answer: A

Explanation:
Concept drift occurs when data patterns change over time, degrading model performance.

NEW QUESTION # 289
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 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.
B. 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.
C. 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.
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: A

NEW QUESTION # 290
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