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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Topic 3: Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Topic 5: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
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NEW QUESTION # 19
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees.
Your model is provided the following:
Answer: B
NEW QUESTION # 20
An automated system makes decisions that produce legal effects for individuals in a jurisdiction with strict data protection regulation. Affected individuals request information about how decisions are reached.
What obligation does this reflect?
Answer: A
Explanation:
Regulations governing automated decision-making generally require that individuals receive meaningful information about the logic involved and the consequences of the processing. This obligation must be designed into the solution rather than added after deployment.
NEW QUESTION # 21
A logistics company wants to optimize its delivery routes while adapting to real-time traffic conditions. Which AI pattern or patterns meets these goals?
Answer: C
Explanation:
Predictive analytics supports route optimization by using real-time traffic data, historical delivery patterns, and operational variables to predict delays and recommend better routes as conditions change.
NEW QUESTION # 22
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, your team doesn't have access to all the data it needs. What's the best course of action?
Answer: A
Explanation:
Moving forward cautiously while recognizing potential pauses allows progress with flexibility, enabling the team to adapt when data access issues arise without derailing the entire project.
NEW QUESTION # 23
Business stakeholders reviewing a deployed classification model ask why a specific customer record received its prediction. The model is a gradient boosted ensemble.
Which approach best addresses this request?
Answer: B
Explanation:
Post-hoc explanation techniques attribute an individual prediction to the contribution of each input feature, addressing instance-level questions without replacing the model. Aggregate performance metrics describe overall behavior but cannot explain a single decision.
NEW QUESTION # 24
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