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| Section | Weight | Objectives |
|---|
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
- 1. Feasibility and ROI analysis
- 2. Define AI business objectives
|
| AI Operationalization and Governance | | - Deployment and lifecycle management
- 1. Continuous improvement and monitoring
- 2. AI governance and responsible AI
|
| AI Model Development and Iteration | | - Model building and validation
- 1. Machine learning / generative AI model development
- 2. Iterative delivery approach
|
| Data for AI | | - Data identification and governance
- 1. Data compliance and ethics
- 2. Data sourcing and selection
|
| Data Preparation for AI | | - Data cleaning and transformation
- 1. Data quality assurance
- 2. Feature engineering and preparation
|
| AI System Testing and Evaluation | | - Model evaluation and monitoring
- 1. Performance evaluation and drift detection
- 2. Explainability and reliability assessment
|
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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q160-Q165):
NEW QUESTION # 160
A healthcare project manager is evaluating whether to implement an AI-powered diagnostic tool.
The initial cost is US$500,000 with an expected return on investment (ROI) of 15% within the first year. The project needs to satisfy multiple stakeholders including hospital administrators and medical staff.
Which method will maximize a positive ROI for the AI implementation?
- A. Acquiring alternatives to the AI solution as a contingency plan
- B. Monitoring AI model performance against key performance indicators
- C. Ensuring all AI and non-AI components are integrated seamlessly
- D. Seeking verbal commitments from interested parties at each project phase
Answer: B
NEW QUESTION # 161
The project team at an IT services company is working on an AI-based customer support chatbot.
To help ensure the chatbot functions effectively, they need to define the required data. Which method meets the project requirements?
- A. Using synthetic data generated from sample customer conversations
- B. Developing a new script based on anticipated customer queries
- C. Integrating feedback from beta customers to refine the model
- D. Gathering historical customer interaction logs for training data
Answer: D
Explanation:
Gathering historical customer interaction logs provides real examples of customer questions, language patterns, issues, intents, and support responses. This helps define and supply the data needed to train an effective customer support chatbot.
NEW QUESTION # 162
During CPMAI Phase II it's important to not only understand the sources of your data, but also what data is required for training as well as identifying the features that are required. When looking to gather data, what approach is best when determining how much data you need?
- A. The "goldilocks" approach
- B. The "more is better" approach
- C. The "less is better" approach
- D. There is no correct approach
Answer: A
Explanation:
The "goldilocks" approach focuses on gathering an optimal amount of data - enough to train effective models without unnecessary excess - balancing quality and quantity for best results.
NEW QUESTION # 163
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?
- A. Automatic model selection
- B. Automatic model assessment
- C. Automatic hyperparameter tuning
- D. Automatic algorithm selection
- E. Iterative modeling and evaluation
Answer: A
NEW QUESTION # 164
An inexperienced team is training a neural network model on a desktop computer which is taking a significant amount of time. Which recommended approach will increase the speed of the model training time?
- A. Divide the dataset into multiple smaller datasets and train the model on each dataset over a desktop computer.
- B. Utilize a contractor to provide the model training portion.
- C. Train the model on graphics processing units (GPUs).
- D. Train the model over multiple desktop computers.
Answer: C
Explanation:
Graphics processing units are designed for highly parallel computations, which makes them well suited for neural network training. Using GPUs can significantly reduce training time compared with relying on a standard desktop CPU.
NEW QUESTION # 165
......
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