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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks |
| Topic 2: Managing AI | 8% | - Managing AI project teams and resources - Risk management in AI projects - Stakeholder management |
| Topic 3: Trustworthy AI | 9% | - Transparency and explainability - Privacy and security - Ethical considerations and bias |
| Topic 4: CPMAI Methodology | 41% | - Phase IV: Model Evaluation
|
| Topic 5: Data for AI | 13% | - Data preparation and preprocessing - Data strategy and governance - DataOps concepts |
| Topic 6: AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
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NEW QUESTION # 173
Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?
Answer: A
NEW QUESTION # 174
A company has recently received a large number of spam emails. Some employees have been clicking on the suspicious emails causing challenges for their IT department. The head of IT wants to create a more robust spam filter and has tasked a team with this project. Which type of algorithm should the project team use?
Answer: B
Explanation:
Spam filtering is a binary classification problem because each email is categorized into one of two classes: spam or not spam. This approach is appropriate when the model must make a yes/no decision for each message.
NEW QUESTION # 175
A project team is working on an AI initiative that requires ensuring the explainability and transparency of their selected algorithms. They have identified the business requirements and stakeholders. What is an effective way to address the project objectives?
Answer: C
Explanation:
Using a rule-based approach supports explainability and transparency because the decision logic is explicit, traceable, and easier for stakeholders to understand. This makes it suitable when the project requires clear reasoning behind algorithmic outputs.
NEW QUESTION # 176
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: C
Explanation:
AutoML tools automate algorithm selection and hyperparameter tuning, enabling less experienced teams to develop models efficiently without waiting for specialized data science resources.
NEW QUESTION # 177
A company is developing a chatbot that can address user queries 24/7 without human intervention. Which AI capability should be implemented?
Answer: A
Explanation:
Conversational AI enables a chatbot to understand user inputs, interpret intent, generate appropriate responses, and maintain interactions continuously without requiring human intervention.
NEW QUESTION # 178
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