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| Section | Weight | Objectives |
|---|---|---|
| Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria |
| Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations |
| Operationalize AI Solution | 17% | - Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes |
| Identify Data Needs | 26% | - Define data requirements and sources - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance |
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
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NEW QUESTION # 102
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs.
Specifically, the images you need are of people in different poses. What is the best way to obtain this data?
Answer: B
Explanation:
Synthetic training data can be generated to create diverse, labeled images of people in various poses, effectively augmenting real-world data without privacy or logistical concerns.
NEW QUESTION # 103
Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data. Which type of machine learning approach would be most appropriate to pick for this problem?
Answer: A
Explanation:
Unsupervised learning is used to discover hidden patterns or structures in unlabeled data, making it appropriate for exploring customer data without predefined outcomes.
NEW QUESTION # 104
A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?
Answer: B
NEW QUESTION # 105
You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?
Answer: A
NEW QUESTION # 106
A project team is using a generative AI assistant to draft stakeholder communications. The drafts are often generic and miss project constraints. What is the most likely cause?
Answer: D
Explanation:
PMI guidance on using GenAI highlights that prompts must provide context, guidance, and constraints; otherwise outputs tend to be vague or unhelpful. If stakeholder communications miss constraints (scope boundaries, timeline, dependencies, risk posture), the most likely cause is insufficient prompt specificity--e.g., missing audience, intent, tone, project phase, constraints, and success criteria. PMI explains that the utility of GenAI outputs is strongly tied to the granularity of input: when prompts lack detail, results often become generic and misaligned with the real need.
In CPMAI-aligned execution, this is addressed by iteratively refining prompts (diverge then converge), adding structured context such as assumptions, constraints, and acceptance criteria, and validating outputs against governance expectations for accuracy and appropriateness.
NEW QUESTION # 107
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