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| Section | Weight | Objectives |
|---|---|---|
| Data Research and Analysis | 15% | - Data collection, preparation and preprocessing - Data quality, bias and validation - Data visualization and insight generation |
| Generative AI Models | 20% | - Generative AI model families and use cases - Model selection and evaluation in AI hubs - Model hosting options and trade-offs - Context windows, token limits and response management |
| AI Ethics and Security | 20% | - Data privacy and security in AI systems - AI-specific threats, risks and misinformation - Responsible AI principles - Compliance and governance frameworks |
| Prompt Engineering | 15% | - Defensive prompting and error mitigation - Advanced prompting techniques - Prompt design principles and best practices |
| AI for Code and Workflow Optimization | 15% | - AI-assisted code generation and improvement - Automation and workflow enhancement - Integration of AI tools in development processes |
| Agentic AI | 15% | - Model Context Protocol (MCP) framework - Human-in-the-loop strategies - Agentic AI vs Generative AI use cases - Agent design principles and capabilities |
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NEW QUESTION # 12
Which metric is commonly used to evaluate classification models?
Answer: A
Explanation:
Accuracy measures how many predictions were correct out of total predictions.
NEW QUESTION # 13
Why are prompts important in generative AI systems?
Answer: B
Explanation:
Well-structured prompts significantly improve response accuracy and relevance.
NEW QUESTION # 14
What is the purpose of tokenization in Large Language Models (LLMs)?
Answer: C
Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.
NEW QUESTION # 15
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?
Answer: B
Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.
NEW QUESTION # 16
Which protocol is commonly used for streaming telemetry in modern networks?
Answer: D
Explanation:
gRPC supports efficient real-time telemetry streaming in modern network automation environments.
NEW QUESTION # 17
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