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| Section | Weight | Objectives |
|---|---|---|
| Operationalize AI Solution | 17% | - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| 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 |
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
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NEW QUESTION # 231
A company plans to operationalize an AI solution. The project manager needs to ensure model performance is meeting selected thresholds before release. What is an effective way to confirm these thresholds before this release?
Answer: D
Explanation:
Testing against validation datasets confirms whether the model meets the selected performance thresholds before release. This provides objective evidence that the model performs reliably on data separate from training data and is ready for operationalization.
NEW QUESTION # 232
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?
Answer: A
Explanation:
Insufficient context prevents the model from understanding the intended goal, constraints, audience, or required details. As a result, the generated responses are likely to be vague, misaligned, or not relevant to the specific task.
NEW QUESTION # 233
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: B
Explanation:
Dimensionality reduction reduces the number of input variables, simplifying the dataset while preserving important information for modeling.
NEW QUESTION # 234
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: B
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 # 235
Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?
Answer: A
Explanation:
Regular model retraining is essential to maintain performance as data changes over time; neglecting this leads to degraded model accuracy.
NEW QUESTION # 236
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