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| Section | Weight | Objectives |
|---|---|---|
| Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability |
| Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions |
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NEW QUESTION # 311
A project team is assessing a customer dataset and finds that 22 percent of records have a null value in the primary address field, and several records list contradictory account open dates.
Which two data quality dimensions are most directly affected?
Answer: D
Explanation:
Missing values in a required field represent a completeness problem, while contradictory values for the same attribute represent a consistency problem. Identifying which specific dimensions are affected determines the appropriate remediation approach during Data Preparation.
NEW QUESTION # 312
An AI project team has identified a gap in their data knowledge and experience. They need to address this issue in order to proceed with their AI implementation. What is the effective solution?
Answer: D
Explanation:
Hiring an external data consultant provides immediate targeted expertise to address the team's data knowledge and experience gap. The consultant can guide the team on data quality, preparation, governance, and implementation practices while also helping build internal capability through training.
NEW QUESTION # 313
Your team is working on an AI enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data, you realize you don't have enough data to train the model. What's the best course of action?
Answer: D
Explanation:
Leveraging third-party pre-trained models can provide the necessary data and capabilities to progress the chatbot project when in-house data is insufficient.
NEW QUESTION # 314
A project team is overseeing the data evaluation for an AI model predicting customer churn. They observed that the model's predictions are biased toward a particular class.
What is an effective technique to mitigate this bias?
Answer: C
Explanation:
Stratified sampling preserves appropriate class proportions when creating training or evaluation subsets. This helps ensure that each class is adequately represented, reducing class-related sampling bias and producing a more balanced assessment of the churn model.
NEW QUESTION # 315
A financial services firm is integrating AI to enhance fraud detection. To oversee data evaluation, the project manager needs to ensure the integrity and accuracy of input data, including transaction histories and customer profiles. Which method provides the results that address the requirements?
Answer: C
Explanation:
Using a fact checklist provides a systematic way to verify the integrity, accuracy, completeness, and reliability of transaction histories and customer profiles before they are used by the fraud detection model.
NEW QUESTION # 316
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