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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| Topic 2: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
| Topic 3: Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
| 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 |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
While developing a demand forecasting model, the team inadvertently includes future sales data during training. The model shows unusually high accuracy during testing but performs poorly in real deployment. What issue has occurred in this scenario?
- A. Model instability
- B. Data leakage
- C. Sampling bias
- D. Concept drift
Correct Answer: B 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?
- A. Governed AI
- B. Explainable AI
- C. Ethical AI
- D. Transparent AI
- E. Responsible AI
Correct Answer: D 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
A company is operationalizing an AI system for automated customer service. The system appears to favor queries in one language over others, despite having been trained on a multilingual dataset. Which two issues should the project lead investigate? (Choose two.)
- A. Check the distribution of training samples per language.
- B. Determine whether the neural network architecture supports multilingual processing.
- C. Presume the dataset is at fault, without conducting a detailed analysis.
- D. Evaluate the system's response times for different languages.
- E. Examine whether the preprocessing techniques varied across languages.
Correct Answer: A,E 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
A team is evaluating different AI models for their project. They are considering error rates and overall performance. If the team had selected a model based solely on the error rate, what would be the outcome?
- A. A better performance across the chosen domains
- B. A balanced performance across all metrics
- C. A potential to overlook other critical performance metrics
- D. An increase in stakeholder satisfaction based on performance
Correct Answer: C 🗳️
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Clean, well-labeled, datasets used for machine learning are partitioned into three subsets:
Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?
- A. Use the same data for all sets
- B. Split by alphabetical order
- C. Split by patterned subsampling
- D. Split by random subsampling
Correct Answer: D 🗳️
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