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Details |
| Topic 1 |
- CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.
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| Topic 2 |
- Domain VI Trustworthy AI: This section is designed for the Project Manager and focuses on ethical, responsible, and transparent AI development. It covers building trustworthy systems, dispelling misconceptions, evaluating real-world ethical concerns, defining responsible frameworks, and implementing mitigation tactics for unintended harms. It addresses data privacy, GDPR compliance, protection of PII, anonymization techniques, security against adversarial threats, and monitoring.
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| Topic 3 |
- Machine Learning: This section is aimed at the Data
- AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.
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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q89-Q94):
NEW QUESTION # 89
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?
- A. Phase II
- B. Phase VI
- C. Phase III
- D. Phase I
- E. Phase IV
- F. Phase V
Answer: C
Explanation:
Phase III: Data Preparation includes the Data Labeling generic task group. Specifically, the Label data task covers "identifying methods for data labeling and engaging in data labeling efforts," which is essential for supervised learning workflows like image recognition.
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NEW QUESTION # 90
You're creating an AI-enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Select all that apply.)
- A. Data Sharing challenges
- B. Data Quality Issues
- C. Change Management Issues
- D. Business Risks
- E. Data Quantity Issues
- F. Privacy Risks
- G. Security Risks
Answer: A,B,F,G
Explanation:
Domain IV: Data for AI - Task 2: Implementing Data Governance and Management mandates establishing data stewardship, management plans, lineage, and master-data practices. Core governance concerns include how data is shared (A), ensuring user privacy (B), guarding against breaches (E), and maintaining high data quality (F). Quantity or change-management issues are operational rather than governance controls.
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NEW QUESTION # 91
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
- A. The volume of the data you collect
- B. The quality of the data you collect
- C. The location/source of your data collection
- D. The makeup of your data team
Answer: D
Explanation:
In Phase II: Data Understanding, CPMAI's Generic Task Groups focus on:
Collecting initial data (identifying sources and volumes) and describing data (location/source) .
Verifying data quality to ensure completeness and correctness .
Team composition (the makeup of your data team) is addressed earlier under Phase I: Assess Situation, not during the Data Understanding phase where data requirements (quality, volume, source) are determined.
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NEW QUESTION # 92
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing.
What type of problem is this?
- A. You are overfitting the data
- B. You are underfitting the data
- C. You have selected the wrong algorithm
- D. You have selected the wrong data
Answer: A
Explanation:
Overfitting occurs when a model learns not only the underlying patterns but also the noise in the training data, causing it to perform well on seen data but poorly on unseen data. The CPMAI Glossary defines overfitting as "a modeling error where a model learns the training data too well, including its noise, resulting in poor performance on new data."
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NEW QUESTION # 93
Your team was given a large dataset and has been tasked with organizing the data by type to make better insights from the results. You are facing problems with the approach that the previous project lead used which was a regression algorithm.
What type of algorithm is the best approach for this project?
- A. Regression
- B. Clustering
- C. Binary (or Binomial) Classification
- D. Multiclass Classification
Answer: B
Explanation:
When the goal is to group or organize unlabeled data into meaningful categories, CPMAI specifies the use of unsupervised clustering algorithms-for example, "if it is determined that an unsupervised clustering algorithm such as K-means will be used, the tool should support such needs" . Clustering is the appropriate method for discovering groupings by type.
NEW QUESTION # 94
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