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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | - Data identification and governance
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI Model Development and Iteration | - Model building and validation
|
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NEW QUESTION # 175
During the business understanding phase, the project manager realizes that the project team has overlooked requirements for the AI model. The model should have a high level of explainability and transparency. What should the project manager do?
Answer: D
Explanation:
Clearly documenting compliance requirements helps ensure explainability and transparency expectations are formally captured during business understanding. This guides later model selection, validation, governance, and stakeholder review so the AI system can meet accountability and regulatory expectations.
NEW QUESTION # 176
Your team is running a simulation-based optimization exercise to increase routing efficiency.
Learning for this exercise is done through "trial and error".
Which type of machine learning approach is being leveraged for this exercise?
Answer: A
NEW QUESTION # 177
You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What's your best course of action?
Answer: C
Explanation:
Obtaining sufficient training examples for each dimension helps ensure the model can learn reliable patterns and improve prediction accuracy in high-dimensional datasets.
NEW QUESTION # 178
You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration.
What is the name for this process?
Answer: A
NEW QUESTION # 179
A project team needs to determine whether to proceed with an AI/machine learning (ML) project by examining various factors. Which factor should be evaluated by the project manager first?
Answer: D
Explanation:
Business objective alignment should be evaluated first because an AI/ML project must address a clearly defined organizational need and deliver measurable business value. Technical decisions such as model selection, data preparation, and optimization should follow only after the project's objectives and expected outcomes are established.
NEW QUESTION # 180
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