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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
| Topic 2: Trustworthy AI | 9% | - Privacy and security - Transparency and explainability - Ethical considerations and bias |
| Topic 3: Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| Topic 4: Data for AI | 13% | - Data strategy and governance - Data preparation and preprocessing - DataOps concepts |
| Topic 5: AI Fundamentals | 16% | - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning |
| Topic 6: Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management |
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NEW QUESTION # 116
Senior management has tasked your group to analyze a data set to uncover insights into the dat
Answer: A
NEW QUESTION # 117
Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?
Answer: C
Explanation:
Transparent AI focuses on providing visibility into how AI systems are built, including data sources and processes, to build user trust and understanding.
NEW QUESTION # 118
An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution. At what point in the project should the requirements be approached?
Answer: C
Explanation:
Compliance with data regulations and explainability standards should be addressed during the business understanding phase so they become formal project requirements from the start. This ensures the AI solution is planned, designed, validated, and operationalized in alignment with legal, ethical, and stakeholder expectations.
NEW QUESTION # 119
An aerospace company is operationalizing an AI system to optimize flight routes. The system must be safe and comply with aviation regulations. The project team is evaluating methods to prevent potential safety risks.
Which method should the project manager use to prevent safety risk issues?
Answer: B
Explanation:
Extensive simulation testing exposes the AI system to a wide range of normal, abnormal, and edge-case flight conditions before operational use. This helps identify unsafe behaviors and failure modes early, allowing the team to address safety risks before they affect real-world flight operations.
NEW QUESTION # 120
An AI project for a financial technology client is at risk due to potential inaccuracies in data aggregation. What is the first step the project manager should take to mitigate the risk?
Answer: B
Explanation:
PMI's CPMAI/PMI-CPMAI approach stresses that risk mitigation for data issues starts in the Data Understanding work: identifying appropriate datasets, evaluating training data requirements, and validating data quality/ground truth before proceeding. In practical PMI terms, the project manager should first understand the data characteristics--sources and ownership, schemas, join keys, aggregation logic, definitions, completeness, and known constraints--because aggregation inaccuracies often come from mismatched definitions, inconsistent granularity, duplicate entities, or transformation errors. This aligns with PMI guidance that teams must "identify data needs,"
"locate and characterize data," and then assess quality attributes like accuracy, completeness, and consistency to determine preparation effort and readiness.
NEW QUESTION # 121
......
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