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| Section | Weight | Objectives |
|---|---|---|
| Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability |
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
| Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy |
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NEW QUESTION # 58
A telecommunications company is adopting an AI-based customer service chatbot. They are concerned about potential quality issues affecting customer satisfaction. What should the project manager do?
Answer: C
Explanation:
From a project management framework (such as PMBOK), quality must be planned in, not inspected in.
When faced with potential quality risks before or during adoption, a project manager's standard first step is to establish a Quality Assurance (QA) Plan. This plan defines quality standards, testing metrics, success criteria, and how quality control will be executed throughout the project lifecycle.
NEW QUESTION # 59
A hospital wants to develop a medical records system with the primary goal of minimizing or eliminating paper records. They have identified where the cognitive AI solution will be applied. In addition, business objectives have been quantified and key performance indicators (KPIs) have been determined.
What else needs to be done to progress to the next Cognitive Project Management for AI (CPMAI) phase?
Answer: D
NEW QUESTION # 60
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
Answer: D
Explanation:
While the data team makeup affects project execution, it has less direct impact on the specific data requirements compared to data quality, volume, and source considerations.
NEW QUESTION # 61
During the business understanding phase, the project manager realizes that the project team has overlooked requirements for the AI model. The model should have a high level of explainability and transparency. What should the project manager do?
Answer: B
Explanation:
Clearly documenting compliance requirements helps ensure explainability and transparency expectations are formally captured during business understanding. This guides later model selection, validation, governance, and stakeholder review so the AI system can meet accountability and regulatory expectations.
NEW QUESTION # 62
A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation. What should the project manager do first?
Answer: C
Explanation:
Assessing data completeness should be done first because the team must verify whether the customer data contains the required fields, records, and coverage before cleaning, enhancing, labeling, or preparing it for the segmentation model.
NEW QUESTION # 63
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