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| Section | Weight | Objectives |
|---|
| Topic 1: AI Operationalization and Governance | | - Deployment and lifecycle management
- 1. AI governance and responsible AI
- 2. Continuous improvement and monitoring
|
| Topic 2: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
- 1. Define AI business objectives
- 2. Feasibility and ROI analysis
|
| Topic 3: Data Preparation for AI | | - Data cleaning and transformation
- 1. Data quality assurance
- 2. Feature engineering and preparation
|
| Topic 4: AI Model Development and Iteration | | - Model building and validation
- 1. Machine learning / generative AI model development
- 2. Iterative delivery approach
|
| Topic 5: Data for AI | | - Data identification and governance
- 1. Data sourcing and selection
- 2. Data compliance and ethics
|
| 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 (Q213-Q218):
NEW QUESTION # 213
In an aerospace project focused on predictive maintenance using AI, the project team is facing challenges in coordinating the AI models' operationalization across various manufacturing sites.
Strong governance and corporate guardrails are established, but each site has different computational capabilities and network latencies. What is an effective method that helps to ensure consistent AI performance across these sites?
- A. Implementing a centralized AI model repository
- B. Utilizing cloud-based AI services uniformly
- C. Using site-specific AI model tuning
- D. Operationalizing a decentralized AI architecture
Answer: C
Explanation:
Site-specific AI model tuning helps adapt the operationalized model to each manufacturing site's infrastructure constraints, including computational capacity and network latency, while still working within the established governance and corporate guardrails.
NEW QUESTION # 214
When building your model you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective.
At what phase of CPMAI is this most appropriate to do this?
- A. Phase V
- B. Phase I
- C. Phase IV
- D. Phase VI
- E. Phase II
- F. Phase III
Answer: A
NEW QUESTION # 215
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. There is no correct approach
- B. The "goldilocks" approach
- C. The "less is better" approach
- D. The "more is better" approach
Answer: B
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 # 216
A project charter states that the AI solution will "significantly improve customer experience." What is the deficiency in this statement?
- A. It does not specify the AI technique to be used
- B. It is not measurable and cannot support a go/no-go decision
- C. It does not reference the applicable regulatory framework
- D. It does not identify the responsible business owner
Answer: B
Explanation:
Success criteria must be specific and measurable so that model evaluation can determine objectively whether the business objective is met. Qualitative statements provide no threshold against which results can be assessed at phase gates.
NEW QUESTION # 217
You have an Anomaly Detection project you're working on and you need a simple approach of clustering data into classified groups. Which algorithm is the best choice given this situation?
- A. Hidden Markov Model
- B. Neural Network
- C. Decision Tree
- D. K-Means Clustering
Answer: D
NEW QUESTION # 218
......
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