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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q69-Q74):

NEW QUESTION # 69
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 # 70
Why are embeddings essential to the retrieval phase of a RAG system?

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

Answer: A

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 # 71
What is the benefit of AI Ops (AIOps) in networking?

A. Removes cloud computing
B. Disables telemetry
C. Automates monitoring and incident analysis
D. Eliminates all routers

Answer: C

Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.

NEW QUESTION # 72
Which action demonstrates the principle of fairness in a generative AI deployment?

A. redacting PII from the training dataset
B. hosting the model in a carbon-neutral data center
C. auditing model outputs for bias
D. requiring users to sign Acceptable Use Policies

Answer: C

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.

NEW QUESTION # 73
What happens when the input provided to an LLM exceeds its context window limit?

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

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 # 74
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