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| Section | Weight | Objectives |
|---|
| AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning
|
| Data for AI | 13% | - DataOps concepts - Data preparation and preprocessing - Data strategy and governance
|
| Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management
|
| Trustworthy AI | 9% | - Privacy and security - Transparency and explainability - Ethical considerations and bias
|
| Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision)
|
| CPMAI Methodology | 41% | - Phase I: Problem Identification
- 1. Identify business needs and solutions
- 2. Evaluate initial AI feasibility
- Phase III: Algorithm Selection & Model Training
- 1. Oversee AI/ML model technique selection
- 2. Manage model training and configuration
- Phase II: Data Identification & Curation
- 1. Assess data readiness and quality
- 2. Identify data needs
- Phase VI: Iteration & Monitoring
- 1. Retraining and model drift management
- 2. Continuous monitoring and iteration
- Phase V: Operationalization
- 1. Plan for deployment and integration
- 2. Define success criteria and monitoring
- Phase IV: Model Evaluation
- 1. Evaluate model performance against metrics
- 2. Oversee AI/ML model QA/QC
|
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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q286-Q291):
NEW QUESTION # 286
Your team is looking for a short term ROI project and decides that an AI-enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition.
What's the best course of action with how to proceed?
- A. Do not move forward until you can determine a clear problem definition.
- B. Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go/No Go decision chart to start your project.
- C. Do not move forward and cancel the project altogether.
- D. Move forward with the project as planned. The problem definition will become clear later on in the project.
Answer: A
NEW QUESTION # 287
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)
- A. Supporting training data
- B. Bias-variance tradeoff
- C. Hyperparameter settings
- D. The algorithm code
Answer: A,C,D
NEW QUESTION # 288
You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team. What's your best course of action?
- A. Use an AutoML solution
- B. Put the project on hold until the resources needed become available
- C. Find a citizen data scientist to help
- D. Outsource the project ASAP
Answer: A
Explanation:
AutoML tools automate algorithm selection and hyperparameter tuning, enabling less experienced teams to develop models efficiently without waiting for specialized data science resources.
NEW QUESTION # 289
An aerospace company is planning to integrate AI for predictive maintenance. The project team needs to identify which parts of the project require cognitive AI solutions. Which method should the project manager perform to meet the objective?
- A. Utilizing an AI-driven workflow engine to automate project segmentation
- B. Implementing a rule-based system to classify AI project elements based on predefined criteria
- C. Conducting a series of workshops with domain experts to map out AI dependencies
- D. Employing a multicriteria decision analysis tool to categorize AI project components
Answer: C
Explanation:
Conducting workshops with domain experts helps identify which predictive maintenance activities require cognitive AI capabilities, such as anomaly detection, failure prediction, and pattern recognition. Domain experts can map dependencies, clarify operational needs, and separate AI- suitable tasks from tasks that can be handled through traditional methods.
NEW QUESTION # 290
Using machine learning and other cognitive approaches to understand how to take past / existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior / interactions / data is a core part to which pattern(s) of AI?
- A. Predictive Analytics & Decision Support
- B. Recognition Pattern
- C. Predictive Analytics & Decision Support and Patterns and Anomalies
- D. Goal Driven Systems
Answer: A
Explanation:
Using machine learning to analyze past behavior and predict future outcomes or assist decision- making is central to the Predictive Analytics & Decision Support pattern in AI.
NEW QUESTION # 291
......
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