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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
AI Ethics and Security		- Ethical considerations in AI 1. Transparency and explainability 2. Bias and fairness - Security threats and mitigations 1. Model security 2. Data privacy
Prompt Engineering	15%	- Prompting techniques 1. Few-shot prompting 2. Chained prompting 3. Iterative prompting - Security and safety 1. Reducing hallucinations 2. Defensive prompting methods 3. Prompt injection attacks 4. Improving response accuracy - Principles and patterns 1. Constraints 2. Instructions 3. Roles - Prompt structures 1. Audio generation 2. Text generation 3. Image generation
Generative AI Models	20%	- Common use cases 1. Code generation 2. Text summarization 3. Content creation - Model selection 1. Reasoning and multimodal tasks 2. Selection from AI hubs - Context windows and token management 1. Token limits 2. Response management - Major generative model families 1. Large language models (LLMs) 2. Diffusion models - Model hosting strategies 1. Cost, latency, privacy, and scalability considerations 2. Cloud-based vs. local deployments - Retrieval Augmented Generation (RAG) 1. Grounding AI outputs in external knowledge 2. Vector databases 3. Embeddings
Agentic AI	20%	- Model Context Protocol (MCP) 1. Context in agentic AI 2. MCP framework primitives - AI agent design principles 1. Orchestration 2. Human-in-the-loop (HITL) strategies - Data transformation and mapping 1. Within AI Agents - Differentiation from Generative AI 1. Use cases comparison 2. Autonomous capabilities
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
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 (Q13-Q18):

NEW QUESTION # 13
What happens when the input provided to an LLM exceeds its context window limit?

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

Answer: A

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 # 14
Which workflow mechanism is being used when a company implements a policy where an AI agent can draft a customer email, but a human must authorize the transmission?

A. injection mitigation
B. data lineage
C. drift monitoring
D. approval gate

Answer: D

Explanation:
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.

NEW QUESTION # 15
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

A. to provide administrative credentials required to bypass MFA requirements
B. to allow the AI agent to automatically resolve transaction deadlocks
C. to allow the AI agent to bypass the integrity constraints of the database
D. to serve as a safety gate against unintended data modifications

Answer: D

Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.

NEW QUESTION # 16
What does NLP stand for in AI?

A. Natural Language Processing
B. Neural Link Protocol
C. Network Layer Processing
D. Native Logic Programming

Answer: A

Explanation:
NLP enables computers to understand and generate human language.

NEW QUESTION # 17
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

A. to define the required parameters and data types the agent must return
B. to encrypt the payload before it is sent to the external service
C. to map relationships between multiple tools in an agent workflow
D. to reduce the token count of the agent's system prompt

Answer: A

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 # 18
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