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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic AI	20%	- Differentiation from Generative AI 1. Use cases comparison 2. Autonomous capabilities - Data transformation and mapping 1. Within AI Agents - Model Context Protocol (MCP) 1. MCP framework primitives 2. Context in agentic AI - AI agent design principles 1. Orchestration 2. Human-in-the-loop (HITL) strategies
Topic 2: AI for Code and Workflow Optimization		- AI-assisted code development 1. Code review and debugging 2. Code generation - Workflow automation 1. Process automation 2. Task optimization
Topic 3: Prompt Engineering	15%	- Prompt structures 1. Audio generation 2. Image generation 3. Text generation - Principles and patterns 1. Instructions 2. Constraints 3. Roles - Security and safety 1. Prompt injection attacks 2. Improving response accuracy 3. Reducing hallucinations 4. Defensive prompting methods - Prompting techniques 1. Iterative prompting 2. Chained prompting 3. Few-shot prompting
Topic 4: AI Ethics and Security		- Ethical considerations in AI 1. Transparency and explainability 2. Bias and fairness - Security threats and mitigations 1. Data privacy 2. Model security
Topic 5: Generative AI Models	20%	- Major generative model families 1. Diffusion models 2. Large language models (LLMs) - Retrieval Augmented Generation (RAG) 1. Vector databases 2. Grounding AI outputs in external knowledge 3. Embeddings - Common use cases 1. Content creation 2. Code generation 3. Text summarization - Context windows and token management 1. Response management 2. Token limits - Model selection 1. Reasoning and multimodal tasks 2. Selection from AI hubs - Model hosting strategies 1. Cost, latency, privacy, and scalability considerations 2. Cloud-based vs. local deployments
Topic 6: Data Research and Analysis		- Data collection and preparation 1. Data cleaning and preprocessing 2. Data gathering methodologies - Data analysis techniques 1. Statistical analysis 2. Data visualization

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Cisco AI Technical Practitioner Sample Questions (Q66-Q71):

NEW QUESTION # 66
Why are embeddings essential to the retrieval phase of a RAG system?

A. They provide a keyword index.
B. They represent semantic meaning.
C. They compress the prompt length.
D. They reduce model hallucination.

Answer: B

Explanation:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.

NEW QUESTION # 67
What is the role of the client in the Model Context Protocol?

A. to act as a tool or service provider
B. to provide resources for task execution
C. to orchestrate interactions with servers
D. to act as an LLM for inferencing

Answer: C

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.

NEW QUESTION # 68
What happens when the input provided to an LLM exceeds its context window limit?

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

Answer: C

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 # 69
A team uses AI to scaffold a new microservice. What does this process involve?

A. removing redundant logic
B. creating automated deployment scripts
C. testing the final production code
D. generating the initial structure

Answer: D

Explanation:
Scaffolding uses AI to generate the initial project structure, boilerplate files, configuration, and basic code layout so the team can start development faster.

NEW QUESTION # 70
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

A. data poisoning
B. disinformation
C. knowledge cut-off
D. misinformation

Answer: C

Explanation:
Knowledge cut-off occurs when a model lacks information about events, standards, or specifications released after its training data period, causing it to provide outdated information.

NEW QUESTION # 71
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