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| Section | Weight | Objectives |
|---|---|---|
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation |
| Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy |
| Identify Data Needs | 26% | - Define data requirements and sources - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance |
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NEW QUESTION # 115
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
Answer: B
NEW QUESTION # 116
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?
Answer: D
Explanation:
Data selection and attribute pruning help reduce dataset size and complexity by focusing on relevant features and samples, making model training more efficient without losing important information.
NEW QUESTION # 117
An IT services company is verifying data quality for an AI project aimed at predicting server downtimes. The project manager needs to decide whether to proceed with data preparation.
Which technique should the project manager use?
Answer: A
Explanation:
Exploratory data analysis helps verify whether the server downtime data is complete, accurate, consistent, relevant, and suitable before moving into data preparation. It allows the project manager to identify missing values, outliers, patterns, anomalies, and quality issues that could affect model development.
NEW QUESTION # 118
You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team.
What's your best course of action?
Answer: B
NEW QUESTION # 119
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same. What is most likely happening here?
Answer: D
Explanation:
Biased datasets can cause the model to discriminate against certain demographics, resulting in unfair or unequal loan denial decisions despite similar attributes.
NEW QUESTION # 120
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