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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Identify Data Needs26%- Define data requirements and sources
- Ensure data quality, privacy, security, and compliance
- Plan data collection, storage, and infrastructure
Topic 2: Manage AI Model Development and Evaluation16%- Address model drift, explainability, and limitations
- Monitor performance, accuracy, and reliability
- Oversee model design, training, and validation
Topic 3: Operationalize AI Solution17%- Deploy AI systems into production
- Establish monitoring, maintenance, and improvement processes
- Manage change, adoption, and governance post-launch
Topic 4: Identify Business Needs and Solutions26%- Align AI initiatives with organizational strategy
- Evaluate feasibility and value of AI solutions
- Define requirements, scope, and success criteria
Topic 5: Support Responsible and Trustworthy AI Efforts15%- Ensure fairness, transparency, accountability
- Establish ethical and governance frameworks
- Manage bias, risk, compliance, and societal impact

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q232-Q237):

NEW QUESTION # 232
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?

Answer: A


NEW QUESTION # 233
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs).
However, they are unsure how to effectively quantify these KPIs. Which two approaches should be used? (Choose two.)

Answer: C,E

Explanation:
Establishing a baseline using historical data comparisons provides a measurable starting point for quantifying improvements in fraud detection accuracy, investigation time, and cost savings.
Continuous performance monitoring then tracks whether the AI system is meeting those KPI targets over time after implementation.


NEW QUESTION # 234
A project team is tasked with developing an AI system to optimize workflow efficiency by automating both cognitive and noncognitive tasks. The AI system will handle various tasks including data analysis and decision-making to routine administrative functions. The AI system needs to be integrated with the existing enterprise software and meet international regulations.
The team needs to manage data from multiple sources with different formats and standards. The AI system needs to evolve as business processes and technologies change. How would the results for the system be different if cognitive and noncognitive tasks are not separated?

Answer: A

Explanation:
When cognitive and noncognitive tasks are not separated, the solution may apply advanced AI methods to simple rule-based or repetitive work. This creates unnecessary complexity, higher cost, and inefficient use of AI capabilities that should be reserved for tasks requiring reasoning, learning, prediction, or decision support.


NEW QUESTION # 235
An aerospace company is evaluating whether their sensor data meets the requirements for an AI- based predictive maintenance system. The project team needs to ensure that the data's accuracy, resolution, and timeliness are adequate to predict equipment failures. Which method addresses the requirements?

Answer: A

Explanation:
Performing a data quality assessment focused on precision and latency directly verifies whether the sensor data is accurate enough, captured at the right resolution, and available quickly enough to support reliable predictive maintenance decisions.


NEW QUESTION # 236
You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Choose all that apply.)

Answer: A,B,C,D,E

Explanation:
A data scientist should have prompt engineering skills, expertise with data manipulation tools, strong critical thinking, solid understanding of algorithms, and strong math skills in calculus and statistics to effectively build and interpret AI models.


NEW QUESTION # 237
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