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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q35-Q40):

NEW QUESTION # 35
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: C

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 # 36
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project. What's wrong with this suggestion?

Answer: C

Explanation:
Data preparation pipelines must be tailored to the specific pattern and model needs of each project, so reusing pipelines without customization can lead to suboptimal results.


NEW QUESTION # 37
As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?

Answer: B

Explanation:
For successful hyperpersonalization, individual profiles must both learn and adapt continuously while being programmed for various purposes to deliver dynamic, personalized experiences.


NEW QUESTION # 38
You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy. What is your next best course of action to address this?

Answer: B

Explanation:
Sharing your prior experiences with how your previous team approached AI projects helps educate senior management on the complexities of AI implementation and the importance of doing AI rather than just buying it.


NEW QUESTION # 39
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?

Answer: C

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 # 40
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