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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q120-Q125):

NEW QUESTION # 120
An IT services company is integrating an AI solution to automate its customer service functions.
The integration team is facing resistance from the customer's employees. Which action should the project manager perform to manage this risk?

A. Conduct all-hands meetings on the benefits.
B. Implement a gradual phased rollout.
C. Offer the option to join another team.
D. Mandate immediate transition from management.

Answer: B

Explanation:
Implementing a gradual phased rollout helps reduce employee resistance by allowing users to adapt incrementally, receive support, provide feedback, and build confidence in the AI solution before full transition.

NEW QUESTION # 121
A project team has a modest dataset and is concerned that a single train-test split may produce an unreliable performance estimate.
What approach should the project manager recommend?

A. Repeat training with different random seeds
B. Apply k-fold cross-validation
C. Increase the proportion of data allocated to testing
D. Use the full dataset for both training and evaluation

Answer: B

Explanation:
K-fold cross-validation partitions the data into k subsets and rotates which subset serves as the validation set, averaging results across folds. This produces a more stable performance estimate and reduces the influence of any single partition.

NEW QUESTION # 122
An organization has been running an AI project using a traditional waterfall approach. After nine months, the delivered model performs poorly against real-world data, and stakeholders are dissatisfied.
What is the primary methodological cause of this outcome?

A. Feedback and validation were deferred until the end of the lifecycle
B. Requirements were not documented in adequate detail
C. The data science team was not co-located with business stakeholders
D. The team lacked sufficient computing resources

Answer: A

Explanation:
AI projects are inherently experimental and data-dependent, so assumptions must be validated continuously. A waterfall approach defers learning until delivery, allowing incorrect assumptions about data and model behavior to persist for months before they are discovered.

NEW QUESTION # 123
During CPMAI Phase II of your project, your team is going through their data collection needs.
One team member wants to make use of pre-trained models while another member is adamantly against it. As the project lead, what should you do?

A. Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
B. Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.
C. Evaluate your data and use only what you have and build all models in house.
D. Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those

Answer: A

Explanation:
Evaluating the data to determine if pre-trained models are appropriate and then researching the best-fit models ensures informed decision-making that can save time and leverage existing resources effectively.

NEW QUESTION # 124
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?

A. Careful algorithm selection that reduces the need for data.
B. Data selection and attribute pruning to reduce overall size and data complexity.
C. Data integration focused on reducing the number of data sources.
D. Data Deduping to reduce overall size and data complexity.

Answer: B

Explanation:
Data selection and attribute pruning help reduce dataset size and complexity by focusing on relevant features and samples, making model training more efficient without losing important information.

NEW QUESTION # 125
......

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