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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
| Topic 2: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Topic 3: Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions |
| Topic 4: 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 5: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
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NEW QUESTION # 202
During the configuration management of an AI/machine learning (ML) model, the team has observed inconsistent performance metrics across different test datasets. What will cause the inconsistency issue?
Answer: C
Explanation:
Overfitting causes the model to learn patterns that are too specific to the training data, so performance may look strong in one dataset but become inconsistent when tested against different datasets.
NEW QUESTION # 203
An AI system recommends loan approval decisions. Regulators require that a qualified employee make the final determination and be able to override the system.
What control does this requirement describe?
Answer: B
Explanation:
Human-in-the-loop places a person in the decision path with authority to review, confirm, or override the system's output. This is a common requirement in regulated, high-consequence domains where full automation is not permitted.
NEW QUESTION # 204
An IT services company is operationalizing an AI-based customer service chatbot. The project manager needs to track the workflow of key resources such as natural language processing experts, cloud computing resources, and integration specialists. What will enable the project manager to visualize and manage the cadence of resource activities?
Answer: D
Explanation:
A kanban board visually tracks resource activities, work stages, and task flow across specialists and technical resources. It helps the project manager manage cadence, identify bottlenecks, and coordinate ongoing chatbot operationalization work.
NEW QUESTION # 205
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Choose all that apply.)
Answer: A,B,C,D,F
Explanation:
Generative AI is well-suited for augmenting human tasks, creating content, augmenting training data, automating content generation programmatically, and powering virtual avatars. Explainable decision-support typically involves different AI techniques focused on transparency rather than generation.
NEW QUESTION # 206
A model achieves 97 percent accuracy during validation but performs poorly in production.
Investigation reveals that a feature in the training set was derived from information that becomes available only after the outcome occurs.
What issue does this describe?
Answer: A
Explanation:
Data leakage occurs when the training data contains information that would not be available at prediction time. This inflates validation performance because the model is effectively learning from the answer, and the advantage disappears once the model is deployed.
NEW QUESTION # 207
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