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| Section | Weight | Objectives |
|---|
| Topic 1: Data for AI | | - Data identification and governance
- 1. Data sourcing and selection
- 2. Data compliance and ethics
|
| Topic 2: Data Preparation for AI | | - Data cleaning and transformation
- 1. Data quality assurance
- 2. Feature engineering and preparation
|
| Topic 3: AI Model Development and Iteration | | - Model building and validation
- 1. Iterative delivery approach
- 2. Machine learning / generative AI model development
|
| Topic 4: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
- 1. Feasibility and ROI analysis
- 2. Define AI business objectives
|
| Topic 5: AI Operationalization and Governance | | - Deployment and lifecycle management
- 1. AI governance and responsible AI
- 2. Continuous improvement and monitoring
|
| Topic 6: AI System Testing and Evaluation | | - Model evaluation and monitoring
- 1. Explainability and reliability assessment
- 2. Performance evaluation and drift detection
|
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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q257-Q262):
NEW QUESTION # 257
Enhancing and cleaning data is an important action during which phase of CPMAI?
- A. Phase VI
- B. Phase II
- C. Phase III
- D. Phase IV
- E. Phase V
- F. Phase I
Answer: F
Explanation:
Enhancing and cleaning data is a key activity during Phase I of the CPMAI (Cognitive Project Management for AI) framework, which focuses on data preparation and exploration to ensure quality input for model development.
NEW QUESTION # 258
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project.
What's the best way to approach this?
- A. Take inventory of all data your company has and use the relevant data
- B. Take all the existing data you have and apply it to this project
- C. Take inventory of all data your team has and use the relevant data
- D. Take all the data your company has as well as purchase additional external data
Answer: A
NEW QUESTION # 259
A telecommunications company is preparing its data for a new AI-driven customer service chatbot. The project manager needs to ensure the data is precisely formatted and cleaned before moving on to the model-building phase. Which principle does this represent?
- A. Implementing phase-specific data augmentation
- B. Performing data preprocessing tasks
- C. Conducting AI readiness assessments
- D. Defining cognitive pattern recognition
Answer: B
Explanation:
Performing data preprocessing tasks includes cleaning, formatting, transforming, and preparing data before model building so the chatbot can train on consistent, usable, and reliable inputs.
NEW QUESTION # 260
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?
- A. Evaluating the data schema and integrating additional data sources
- B. Analyzing data completeness and conducting feature engineering
- C. Implementing a data governance framework to ensure compliance
- D. Performing a data quality assessment focusing on precision and latency
Answer: D
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 # 261
Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data. Which type of machine learning approach would be most appropriate to pick for this problem?
- A. Reinforcement Learning
- B. Unsupervised Learning
- C. All would work equally well
- D. Supervised Learning
Answer: B
Explanation:
Unsupervised learning is used to discover hidden patterns or structures in unlabeled data, making it appropriate for exploring customer data without predefined outcomes.
NEW QUESTION # 262
......
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