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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering | 15% | - Prompt design principles and best practices - Advanced prompting techniques - Defensive prompting and error mitigation |
| Topic 2: Generative AI Models | 20% | - Model hosting options and trade-offs - Model selection and evaluation in AI hubs - Generative AI model families and use cases - Context windows, token limits and response management |
| Topic 3: AI Ethics and Security | 20% | - Responsible AI principles - Compliance and governance frameworks - AI-specific threats, risks and misinformation - Data privacy and security in AI systems |
| Topic 4: Agentic AI | 15% | - Agent design principles and capabilities - Agentic AI vs Generative AI use cases - Model Context Protocol (MCP) framework - Human-in-the-loop strategies |
| Topic 5: Data Research and Analysis | 15% | - Data quality, bias and validation - Data visualization and insight generation - Data collection, preparation and preprocessing |
| Topic 6: AI for Code and Workflow Optimization | 15% | - Automation and workflow enhancement - Integration of AI tools in development processes - AI-assisted code generation and improvement |
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NEW QUESTION # 47
What is hallucination in generative AI?
Answer: A
Explanation:
Hallucination occurs when an AI confidently produces false or misleading outputs.
NEW QUESTION # 48
What is the role of the client in the Model Context Protocol?
Answer: A
NEW QUESTION # 49
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?
Answer: C
Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.
NEW QUESTION # 50
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?
Answer: D
Explanation:
Describing the situation means providing relevant context, background information, and constraints so the AI can generate a response that fits the specific scenario.
NEW QUESTION # 51
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?
Answer: D
Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.
NEW QUESTION # 52
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