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| Section | Weight | Objectives |
|---|---|---|
| Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation |
| Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks |
| Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production |
| 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 |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
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NEW QUESTION # 84
A project team is trying to determine the most suitable environment to operationalize their AI/machine learning (ML) solution. They need to consider various factors to help ensure a successful implementation. What should the project manager do?
Answer: B
Explanation:
Identifying the end users and how they will interact with the AI/ML solution helps determine the right operational environment, including where the model should run, how outputs will be delivered, what workflows must be supported, and what usability or integration requirements must be met.
NEW QUESTION # 85
A project manager at a consulting firm is tasked with facilitating the creation of an AI-driven predictive maintenance system for a manufacturing client. Identifying data sources and locations is crucial for the project's success. Which method will help to ensure the project manager meets the objectives?
Answer: A
Explanation:
Creating a detailed data flow diagram helps identify where data comes from, where it is stored, how it moves between systems, and how it will be used in the predictive maintenance solution.
This directly supports locating and validating the required data sources for the AI project.
NEW QUESTION # 86
You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy.
What is your next best course of action to address this?
Answer: A
NEW QUESTION # 87
A telecommunications company is considering an AI solution to improve customer service through automated chatbots. The project team is assessing the feasibility of the AI solution by examining its potential scalability and effectiveness.
What will present the highest risk to the company?
Answer: A
Explanation:
Breaching customer data privacy regulations creates the most significant organizational risk because it can lead to legal penalties, regulatory sanctions, reputational damage, and loss of customer trust. Unlike capacity, skills, or integration issues - which are technical problems that can be remediated within the project - a privacy violation has irreversible consequences that extend beyond the project to the entire company. Privacy compliance must therefore be a primary consideration when assessing an AI chatbot solution in a regulated industry.
NEW QUESTION # 88
An energy technology company is in the data preparation phase of developing an AI model to predict equipment maintenance needs. The project manager is deciding how to handle data from multiple formats. What is an effective technique the project manager should use?
Answer: A
Explanation:
Schema mapping is effective when data comes from multiple formats because it aligns fields, structures, and relationships into a consistent format that the AI model can use during preparation and training.
NEW QUESTION # 89
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