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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Support Responsible and Trustworthy AI Efforts15%- Establish ethical and governance frameworks
- Ensure fairness, transparency, accountability
- Manage bias, risk, compliance, and societal impact
Manage AI Model Development and Evaluation16%- Oversee model design, training, and validation
- Address model drift, explainability, and limitations
- Monitor performance, accuracy, and reliability
Operationalize AI Solution17%- Establish monitoring, maintenance, and improvement processes
- Manage change, adoption, and governance post-launch
- Deploy AI systems into production
Identify Business Needs and Solutions26%- Define requirements, scope, and success criteria
- Align AI initiatives with organizational strategy
- Evaluate feasibility and value of AI solutions
Identify Data Needs26%- Ensure data quality, privacy, security, and compliance
- Plan data collection, storage, and infrastructure
- Define data requirements and sources

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

NEW QUESTION # 285
Your team is looking to develop an RPA bot to help with back-office processes such as data entry. What type of bot should your team be creating?

Answer: B

Explanation:
An unattended bot operates autonomously without human intervention, making it ideal for back- office tasks like data entry that are repetitive and do not require user interaction.


NEW QUESTION # 286
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?

Answer: D

Explanation:
Purchasing additional labeled data from a reputable third party is the most direct way to obtain the specific, high-quality data needed to improve model training when existing data is insufficient.


NEW QUESTION # 287
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?

Answer: B

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 # 288
Before developing a complex model, a project team builds a simple rules-based version of the solution using existing business logic.
What is the primary purpose of this?

Answer: D

Explanation:
A baseline defines the performance level achievable without machine learning. If a complex model does not meaningfully exceed the baseline, the additional development, infrastructure, and maintenance cost cannot be justified.


NEW QUESTION # 289
A financial services firm is integrating AI to enhance fraud detection. To oversee data evaluation, the project manager needs to ensure the integrity and accuracy of input data, including transaction histories and customer profiles. Which method provides the results that address the requirements?

Answer: C

Explanation:
Using a fact checklist provides a systematic way to verify the integrity, accuracy, completeness, and reliability of transaction histories and customer profiles before they are used by the fraud detection model.


NEW QUESTION # 290
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