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| Section | Weight | Objectives |
|---|---|---|
| Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation |
| Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
| Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria |
| Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
| Operationalize AI Solution | 17% | - Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes |
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NEW QUESTION # 240
A logistics company is operationalizing an AI solution to optimize delivery routes. The project manager needs to gather up-to-date information on traffic patterns, delivery schedules, and vehicle performance. Which method will integrate these diverse data types?
Answer: C
Explanation:
Implementing a real-time data processing framework supports continuous ingestion and integration of current traffic patterns, delivery schedules, and vehicle performance data. This enables the route optimization system to use timely, diverse operational data for accurate decision-making.
NEW QUESTION # 241
You are working for a large multinational organization and have been assigned to a new project.
For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address (Select all that apply.):
Answer: A,B,C,D
NEW QUESTION # 242
An organization is considering multiple AI solutions to ensure regulatory compliance. What is an effective technique that helps to ensure the AI solution adheres to compliance requirements?
Answer: B
Explanation:
Continuous compliance monitoring provides ongoing oversight of the AI solution against applicable regulatory requirements. It helps identify compliance deviations as they occur and supports timely corrective action as regulations, data, models, and system behavior change.
NEW QUESTION # 243
An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur?
Answer: C
Explanation:
When a go/no-go assessment shows insufficient technology and data factors, the team should not proceed until key readiness concerns are addressed. Verifying data quality and stakeholder alignment helps confirm whether the project can meet business goals, technical needs, and implementation expectations before development continues.
NEW QUESTION # 244
Your team is planning an AI enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions. What stage of CPMAI is the team currently working on?
Answer: C
Explanation:
Phase I of CPMAI focuses on business understanding, including assessing AI project feasibility through Go/No Go decisions before development begins.
NEW QUESTION # 245
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