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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration
Topic 2: Ethics and Security	15%	- AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - Data privacy and governance 1. Secure AI data handling - Responsible AI principles 1. Fairness, transparency, accountability 2. Bias mitigation
Topic 3: AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Topic 4: AI for Code and Workflow Optimization		- Automation workflows 1. Workflow orchestration concepts 2. API integration basics - AI-assisted software development 1. SDLC integration 2. Code generation and debugging
Topic 5: Generative AI Models	20%	- Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - Model hosting and architecture 1. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations - Model fundamentals and use cases 1. Use cases: summarization, code generation, content creation 2. LLMs and diffusion models overview
Topic 6: Prompt Engineering	15%	- Prompt design principles 1. Roles, instructions, constraints - Prompting techniques 1. Chained and iterative prompting 2. Few-shot prompting - Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks

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Cisco AI Technical Practitioner Sample Questions (Q34-Q39):

NEW QUESTION # 34
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?

A. code scaffolding
B. continuous integration optimization
C. automated unit testing
D. AI-assisted debugging

Answer: D

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 # 35
What happens when the input provided to an LLM exceeds its context window limit?

A. The model increases its context window size.
B. The model switches to a different algorithm.
C. The model caches the overflow data.
D. The model loses the earliest part of the text.

Answer: D

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.

NEW QUESTION # 36
What is the role of an MCP server in the MCP open standard?

A. to expose tools and resources to an AI model
B. to route requests between different AI providers
C. to host the LLM and perform token inference
D. to provide the GUI for the end-user interaction

Answer: A

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.

NEW QUESTION # 37
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

A. defining a persona
B. using few-shot
C. stating the purpose
D. describing the situation

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 # 38
What is fine-tuning in generative AI?

A. Encrypting prompts
B. Formatting storage drives
C. Training a pre-trained model on specialized data
D. Replacing CPUs

Answer: C

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.

NEW QUESTION # 39
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