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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: AI Model Development and Iteration | | - Model building and validation
- 1. Iterative delivery approach
- 2. Machine learning / generative AI model development
|
| Topic 3: AI Operationalization and Governance | | - Deployment and lifecycle management
- 1. AI governance and responsible AI
- 2. Continuous improvement and monitoring
|
| Topic 4: Data for AI | | - Data identification and governance
- 1. Data sourcing and selection
- 2. Data compliance and ethics
|
| Topic 5: Data Preparation for AI | | - Data cleaning and transformation
- 1. Feature engineering and preparation
- 2. Data quality assurance
|
| Topic 6: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
- 1. Feasibility and ROI analysis
- 2. Define AI business objectives
|
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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q107-Q112):
NEW QUESTION # 107
During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates. Which action will identify the cause of the performance decline?
- A. Increasing the amount of regularization to prevent overfitting
- B. Checking for issues in the data preprocessing pipeline that may have introduced noise
- C. Analyzing the distribution of real world data for potential shifts
- D. Reviewing recent changes made to the model's architecture and parameters
Answer: C
Explanation:
Analyzing the distribution of real-world data helps determine whether data drift has occurred. A shift between the data the model was trained on and the data it now receives can reduce accuracy and increase error rates, even when the model previously performed well.
NEW QUESTION # 108
A manufacturing firm is planning to implement a network of intelligent machines to increase efficiency on the assembly line. The machines are equipped with advanced AI capabilities including precision assembly, quality control for predictive maintenance, and real-time data analysis. The intelligent machines should enhance operational efficiency, reduce downtime, and improve product quality. There needs to be seamless communication between the machines and existing systems, compliance with industry regulations, and a managed transition for the workforce. What is a beneficial outcome of using intelligent machines in this environment?
- A. Higher investment costs without immediate returns
- B. Over-reliance on technology leading to skill degradation
- C. Scalability and flexibility in production
- D. Increased vulnerability to cybersecurity threats
Answer: C
Explanation:
Intelligent machines can improve scalability and flexibility by adapting production processes, supporting real-time analysis, enabling predictive maintenance, and coordinating with existing systems to increase efficiency, reduce downtime, and improve product quality.
NEW QUESTION # 109
Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?
- A. Support Vector Machines (SVM)
- B. Neural Networks
- C. Naive Bayes
- D. Gaussian Mixture
Answer: B
Explanation:
Neural networks typically require more training time, computational resources, and complexity compared to other algorithms, making them less suitable when training time, cost, and complexity are major constraints.
NEW QUESTION # 110
An IT services company project manager is creating an AI project scope statement. They need to include details on the environments, devices, and personnel that will use the AI solution. What should the project manager do?
- A. Develop a comprehensive usage scenario analysis.
- B. Gain stakeholder buy-in to proceed with the project.
- C. Perform a detailed technical requirements audit for the scope statement.
- D. Create an AI efficacy program to complete the scope statement.
Answer: A
Explanation:
Developing a comprehensive usage scenario analysis defines how the AI solution will be used, including the operating environments, devices, user roles, workflows, and personnel involved.
This provides the practical context needed for a complete AI project scope statement.
NEW QUESTION # 111
Your team is working on an AI-enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data you realize you don't have enough data to train the model.
What's the best course of action?
- A. Ask your customer service team to generate additional data for you to use for the project
- B. Do not move forward with the project
- C. Use the data that you have and keep the project moving
- D. Research what Third-Party Models are available and purchase them to keep the project moving
Answer: D
NEW QUESTION # 112
......
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