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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy |
| Topic 2: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
| Topic 3: Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability |
| Topic 4: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Topic 5: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
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NEW QUESTION # 317
A healthcare organization plans to implement an AI system to assist with patient diagnosis. The project manager needs to conduct an ethical risk assessment to ensure the AI system does not propagate bias. What is an effective method that meets the project manager's objective?
Answer: B
Explanation:
Conducting bias audits during development directly supports an ethical risk assessment by systematically testing whether the AI system produces biased outcomes before it is deployed.
This helps identify, measure, and address bias risks early in the diagnostic workflow.
NEW QUESTION # 318
A project team plans to use customer records containing names, national identification numbers, and health information to train a model. The records were originally collected for billing purposes.
What is the primary concern the project manager must address?
Answer: D
Explanation:
Data protection frameworks restrict using personal data for purposes beyond those for which it was collected without a valid lawful basis. The project manager must confirm consent, legal basis, and applicable safeguards before the data enters the pipeline.
NEW QUESTION # 319
A project lead is trying to work on a supervised learning binary classification problem to determine if emails are spam or not. They are working with an inexperienced team and this is their first AI project. What is an effective approach the project lead should use?
Answer: A
Explanation:
Naive Bayes is a simple, well-established algorithm commonly used for spam classification and other text-based supervised learning tasks. It is appropriate for an inexperienced team because it is easier to implement, interpret, and validate while still being effective for binary email classification.
NEW QUESTION # 320
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?
Answer: D
NEW QUESTION # 321
Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?
Answer: C
Explanation:
Regular model retraining is essential to maintain performance as data changes over time; neglecting this leads to degraded model accuracy.
NEW QUESTION # 322
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