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Last Updated: Aug 28, 2026
No. of Questions: 324 Questions & Answers with Testing Engine
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
| Topic 2: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| Topic 3: Operationalize AI Solution | 17% | - Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes |
| Topic 4: Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
| Topic 5: Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions |
Question 1
A hospital wants to develop a medical records system with the primary goal of minimizing or eliminating paper records. They have identified where the cognitive AI solution will be applied. In addition, business objectives have been quantified and key performance indicators (KPI) have been determined. What else needs to be done to progress to the next Cognitive Project Management for AI (CPMAI) phase?
A. Begin prototype development
B. Determine the project ROI
C. Create interdepartmental strategies
D. Explore external data sources
Question 2
A project manager is evaluating the readiness of the team for an AI operationalization project.
They have identified a lack of experience with AI and data knowledge. If this issue is neglected, what is the outcome?
A. The project will face significant delays due to learning curves.
B. The project will require frequent adjustments to the AI models.
C. The project will proceed smoothly with existing knowledge.
D. The project will achieve its objectives with external consultant help.
Question 3
Business stakeholders reviewing a deployed classification model ask why a specific customer record received its prediction. The model is a gradient boosted ensemble.
Which approach best addresses this request?
A. Publish the feature list used during training
B. Provide the model's overall accuracy and confusion matrix
C. Replace the model with logistic regression
D. Apply a post-hoc explanation technique such as SHAP or LIME
Question 4
An AI recommendation system has been in production for four months. The project manager wants to ensure the solution continues to deliver business value.
What is the most effective mechanism?
A. Schedule an annual model audit
B. Compare monthly model accuracy against the original evaluation results
C. Survey users quarterly about their satisfaction with recommendations
D. Establish a continuous feedback loop that captures outcomes and feeds monitoring and retraining decisions
Question 5
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?
A. Understanding which algorithms are best for your data needs.
B. Collect enormous amounts of data - the more data the better.
C. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
D. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
Solutions:
| Question 1 Answer: B | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: C |
Flora
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Marcia
Odelette
Sebastiane
Wanda
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