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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation |
| Topic 2: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production |
| Topic 3: Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
| Topic 5: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
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NEW QUESTION # 19
A government agency is developing an AI system to predict infrastructure failures. The data team needs to ensure that the dataset includes historical maintenance records, environmental data, and equipment usage logs. However, they are encountering issues with data from multiple sources being in different formats. Which two methods should be applied to meet the data team's objectives? (Choose two.)
Answer: B,E
Explanation:
A multiformat data integration tool helps combine maintenance records, environmental data, and equipment usage logs from different systems and formats. A data normalization process then standardizes those inputs into consistent structures and values so the dataset can be used reliably for AI model development.
NEW QUESTION # 20
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?
Answer: C
Explanation:
Overfitting occurs when a model is too sensitive to training data fluctuations, capturing noise instead of general patterns, which reduces its ability to generalize to new data.
NEW QUESTION # 21
You're creating an AI-enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Select all that apply.)
Answer: A,C,D,E,F
NEW QUESTION # 22
You have been tasked at your organization to manage a large language model (LLM) project.
Identify what LLMs are useful for. (Select all that apply.)
Answer: A,B,C,D,E,F
NEW QUESTION # 23
A healthcare provider is operationalizing an AI tool to assist in diagnostic processes. To ensure robust model governance, they need to address data privacy and ethical considerations. What should the project manager do?
Answer: A
Explanation:
Developing a detailed privacy impact assessment helps identify, evaluate, and mitigate privacy and ethical risks before and during operationalization. For a healthcare diagnostic AI tool, this supports responsible data handling, regulatory compliance, patient protection, and strong model governance.
NEW QUESTION # 24
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