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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Topic 2: Data for AI | - Data identification and governance
| |
| Topic 3: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 4: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 5: AI Model Development and Iteration | - Model building and validation
| |
| Topic 6: AI System Testing and Evaluation | - Model evaluation and monitoring
|
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NEW QUESTION # 19
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 # 20
An inexperienced team is training a neural network model on a desktop computer which is taking a significant amount of time. Which recommended approach will increase the speed of the model training time?
Answer: C
Explanation:
Graphics processing units are designed for highly parallel computations, which makes them well suited for neural network training. Using GPUs can significantly reduce training time compared with relying on a standard desktop CPU.
NEW QUESTION # 21
A model has passed evaluation and is approved for production. The project team has no defined process for versioning models, tracking which data version produced them, or rolling back a deployment.
What does this project lack?
Answer: B
Explanation:
Model versioning, data lineage, reproducibility, and rollback capability are core MLOps practices supporting the Model Operationalization phase. Without them, the organization cannot reliably reproduce, audit, or recover a deployed model.
NEW QUESTION # 22
In the finance sector, a company is implementing an AI system for credit risk assessment. The project manager needs to identify the data subject matter experts (SMEs) who can help to ensure the accuracy and reliability of the model. What is an effective method to achieve this objective?
Answer: B
Explanation:
Internal data analysts and financial experts are best positioned to validate the relevance, quality, and interpretation of credit risk data because they understand both the technical data characteristics and the financial risk context needed for a reliable model.
NEW QUESTION # 23
You're in charge of marketing at your organization and you've been tasked with using AI to help create marketing images. What's a good solution for this need?
Answer: A
Explanation:
Generative AI is designed to create new content, such as marketing images, by learning patterns from existing data and generating novel outputs.
NEW QUESTION # 24
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