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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Research and Analysis | 15% | - Data quality, bias and validation - Data collection, preparation and preprocessing - Data visualization and insight generation |
| Topic 2: AI for Code and Workflow Optimization | 15% | - Integration of AI tools in development processes - AI-assisted code generation and improvement - Automation and workflow enhancement |
| Topic 3: Prompt Engineering | 15% | - Prompt design principles and best practices - Defensive prompting and error mitigation - Advanced prompting techniques |
| Topic 4: AI Ethics and Security | 20% | - Responsible AI principles - AI-specific threats, risks and misinformation - Data privacy and security in AI systems - Compliance and governance frameworks |
| Topic 5: Agentic AI | 15% | - Agentic AI vs Generative AI use cases - Model Context Protocol (MCP) framework - Human-in-the-loop strategies - Agent design principles and capabilities |
| Topic 6: Generative AI Models | 20% | - Generative AI model families and use cases - Context windows, token limits and response management - Model hosting options and trade-offs - Model selection and evaluation in AI hubs |
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NEW QUESTION # 21
Which Cisco technology is commonly associated with network observability and telemetry?
Answer: D
Explanation:
NetFlow provides visibility into traffic flows and is widely used for monitoring, analytics, and AI- driven operations.
NEW QUESTION # 22
What is the benefit of AI Ops (AIOps) in networking?
Answer: A
Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.
NEW QUESTION # 23
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?
Answer: A
Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.
NEW QUESTION # 24
What is the main advantage of cloud-based AI services?
Answer: A
Explanation:
Cloud AI platforms provide scalable infrastructure and elastic computing power for AI workloads.
NEW QUESTION # 25
What is fine-tuning in generative AI?
Answer: C
Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.
NEW QUESTION # 26
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