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| Section | Weight | Objectives |
|---|
| Topic 1: AI System Testing and Evaluation | | - Model evaluation and monitoring
- 1. Explainability and reliability assessment
- 2. Performance evaluation and drift detection
|
| Topic 2: Data for AI | | - Data identification and governance
- 1. Data sourcing and selection
- 2. Data compliance and ethics
|
| Topic 3: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
- 1. Define AI business objectives
- 2. Feasibility and ROI analysis
|
| Topic 4: Data Preparation for AI | | - Data cleaning and transformation
- 1. Data quality assurance
- 2. Feature engineering and preparation
|
| Topic 5: AI Operationalization and Governance | | - Deployment and lifecycle management
- 1. Continuous improvement and monitoring
- 2. AI governance and responsible AI
|
| Topic 6: AI Model Development and Iteration | | - Model building and validation
- 1. Iterative delivery approach
- 2. Machine learning / generative AI model development
|
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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q100-Q105):
NEW QUESTION # 100
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions.
What is the best approach for handling this project?
- A. Run it as a hybrid approach and some phases are run separately while other phases are combined together
- B. Run it as one project, combining teams, data requirements, and project needs
- C. Run each pattern as its own project, with their own CPMAI phase iterations, data requirements, and project needs
- D. Run each pattern in isolation, with separate teams
Answer: A
NEW QUESTION # 101
A company is evaluating whether to implement AI for a project. They have defined their business objectives and determined the AI capability they want to use. Which action will enable the project manager to move forward with the project?
- A. Conducting a go/no-go assessment
- B. Identifying the contingency procedures
- C. Implementing a preliminary version of the AI solution
- D. Conducting a data quality assessment
Answer: A
Explanation:
Conducting a go/no-go assessment confirms whether the AI project should proceed after the business objectives and desired AI capability have been defined. It evaluates readiness, feasibility, value, risks, and whether AI is the appropriate solution before committing further resources.
NEW QUESTION # 102
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem. Which of the following is the best approach?
- A. Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
- B. When in doubt, don't apply any pattern of AI
- C. When in doubt, go with the patterns and anomalies pattern as all AI projects are about pattern matching.
- D. Apply every pattern to the project.
Answer: A
Explanation:
Identifying the specific goal of the project and matching it to the appropriate AI pattern(s) ensures the solution aligns with the problem's needs and maximizes effectiveness.
NEW QUESTION # 103
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
Answer: D
Explanation:
Random subsampling ensures that training, validation, and test sets are representative and unbiased samples of the overall data, which helps the model generalize well and provides reliable evaluation.
NEW QUESTION # 104
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?
- A. Perform Data Transformation & Multiplication
- B. Perform Data Anonymization
- C. Perform Data Sampling
- D. Purchase the data from a third party
Answer: A
NEW QUESTION # 105
......
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