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| Section | Weight | Objectives |
|---|---|---|
| Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
| Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability |
| Operationalize AI Solution | 17% | - Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes |
| Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions |
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
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NEW QUESTION # 24
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 (KPI) have been determined. What else needs to be done to progress to the next Cognitive Project Management for AI (CPMAI) phase?
Answer: B
Explanation:
Determining the project ROI helps confirm that the quantified business objectives and KPIs justify continuing the AI initiative. This supports the business case and provides a measurable basis for progressing to the next CPMAI phase.
NEW QUESTION # 25
A fraud detection system must minimize false positives to avoid incorrectly flagging legitimate transactions. The team is selecting evaluation metrics to optimize model performance accordingly. Which metric should be prioritized in this scenario?
Answer: C
Explanation:
Precision reduces false positives, aligning with the requirement.
NEW QUESTION # 26
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: B
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 # 27
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?
Answer: B
Explanation:
Iterative modeling and evaluation require human judgment and domain expertise to interpret results and make strategic decisions, which AutoML tools cannot fully automate effectively.
NEW QUESTION # 28
You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?
Answer: B
NEW QUESTION # 29
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