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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
| Topic 2: Managing AI | 8% | - Managing AI project teams and resources - Risk management in AI projects - Stakeholder management |
| Topic 3: Machine Learning | 13% | - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning |
| Topic 4: CPMAI Methodology | 41% | - Phase I: Problem Identification
|
| Topic 5: Trustworthy AI | 9% | - Transparency and explainability - Privacy and security - Ethical considerations and bias |
| Topic 6: AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning - AI capabilities and limitations |
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NEW QUESTION # 265
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?
Answer: E
Explanation:
Transparent AI includes mechanisms that allow users to understand and contest AI decisions, ensuring openness and accountability in the system.
NEW QUESTION # 266
Your team is testing the NLP model they just created to make sure it's performing as expected.
Some of your team members want to move this model to production and move to the next iteration.
What's wrong with this workflow?
Answer: C
NEW QUESTION # 267
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?
Answer: B
NEW QUESTION # 268
A logistics company is operationalizing an AI solution to optimize delivery routes. The project manager needs to gather up-to-date information on traffic patterns, delivery schedules, and vehicle performance. Which method will integrate these diverse data types?
Answer: B
Explanation:
Implementing a real-time data processing framework supports continuous ingestion and integration of current traffic patterns, delivery schedules, and vehicle performance data. This enables the route optimization system to use timely, diverse operational data for accurate decision-making.
NEW QUESTION # 269
A data science team has produced working models through a series of ad hoc notebooks with hard-coded file paths and manually executed preprocessing steps.
What risk does this create for operationalization?
Answer: A
Explanation:
Manual, undocumented, and non-parameterized workflows cannot be reliably reproduced or automated in a production environment. This technical debt must be resolved through pipeline engineering before the model can be dependably operationalized.
NEW QUESTION # 270
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