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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation for AI | - Data cleaning and transformation
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| AI Model Development and Iteration | - Model building and validation
| |
| Data for AI | - Data identification and governance
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
|
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NEW QUESTION # 165
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?
Answer: C
Explanation:
Using Generative AI results to enhance and accelerate your work represents Augmented Intelligence, where AI assists and amplifies human capabilities without fully replacing human decision-making.
NEW QUESTION # 166
A telecommunications company is preparing its data for a new AI-driven customer service chatbot. The project manager needs to ensure the data is precisely formatted and cleaned before moving on to the model-building phase. Which principle does this represent?
Answer: A
Explanation:
Performing data preprocessing tasks includes cleaning, formatting, transforming, and preparing data before model building so the chatbot can train on consistent, usable, and reliable inputs.
NEW QUESTION # 167
Your team is starting a new facial recognition project and you want to ensure that the project is being done with Trustworthy AI in mind. At what phase of CPMAI would Trustworthy AI be considered?
Answer: A
Explanation:
Trustworthy AI principles must be integrated throughout all phases of the CPMAI lifecycle to ensure fairness, transparency, accountability, and ethical considerations are maintained from start to finish.
NEW QUESTION # 168
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:
Answer: A
Explanation:
When algorithms lack inherent explainability, providing interpretable AI results helps users understand cause and effect, improving trust and transparency.
NEW QUESTION # 169
A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation. What should the project manager do first?
Answer: A
Explanation:
Assessing data completeness should be done first because the team must verify whether the customer data contains the required fields, records, and coverage before cleaning, enhancing, labeling, or preparing it for the segmentation model.
NEW QUESTION # 170
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