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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: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks |
| Topic 3: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
| 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: Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy |
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NEW QUESTION # 305
A team is evaluating different AI models for their project. They are considering error rates and overall performance. If the team had selected a model based solely on the error rate, what would be the outcome?
Answer: B
Explanation:
Selecting a model only by error rate can ignore other important measures such as precision, recall, fairness, robustness, explainability, latency, and business impact. A model may have a low error rate while still performing poorly against the broader criteria needed for project success.
NEW QUESTION # 306
A manufacturing company is operationalizing an AI-driven quality control system. The project manager needs to ensure data privacy and regulatory compliance due to the critical nature in protecting sensitive operational data. What is an effective technique that addresses these requirements?
Answer: D
Explanation:
Applying data anonymization protects sensitive operational data by removing or masking identifying information before it is used in the AI quality control system. This supports data privacy and helps meet regulatory compliance requirements while still allowing the dataset to be analyzed.
NEW QUESTION # 307
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project.
What's the best way to approach this?
Answer: A
NEW QUESTION # 308
A financial institution is implementing a new AI system for fraud detection. The project team must ensure the data meets the needs of the AI solution by verifying data quality, completeness, and relevance. They have access to various internal and external data sources. Which method addresses the project team's objectives?
Answer: C
Explanation:
Conducting a comprehensive data audit and cleansing process verifies whether internal and external fraud-related data is accurate, complete, relevant, and usable for the AI solution before model development or operationalization.
NEW QUESTION # 309
An energy technology company is in the data preparation phase of developing an AI model to predict equipment maintenance needs. The project manager is deciding how to handle data from multiple formats. What is an effective technique the project manager should use?
Answer: D
Explanation:
Schema mapping is effective when data comes from multiple formats because it aligns fields, structures, and relationships into a consistent format that the AI model can use during preparation and training.
NEW QUESTION # 310
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