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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which activity is an agentic AI use case compared to a standard generative AI use case?

A. translating a short email
B. writing a poem about routing
C. generating a meeting summary
D. autonomously fixing a build

Answer: D

Explanation:
Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.

NEW QUESTION # 47
How does context window management help control the cost of a long-running AI conversation?

A. by capping the cumulative number of input tokens sent with each new prompt
B. by increasing the context window size to reduce the total number of API calls
C. by switching to an algorithm that reduces the character-to-token ratio
D. by automatically switching to a lower-cost model as the conversation history grows

Answer: A

Explanation:
Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.

NEW QUESTION # 48
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

A. Tokenization has no impact because pricing is based on a flat-rate subscription.
B. Costs are determined by the number of API calls.
C. The number of tokens determines the per-request cost.
D. Tokenization increases cost by requiring additional GPU run time.

Answer: C

Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.

NEW QUESTION # 49
Why are prompts important in generative AI systems?

A. They determine model behavior and output quality
B. They encrypt AI hardware
C. They eliminate hallucinations completely
D. They replace training datasets

Answer: A

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.

NEW QUESTION # 50
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

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

Answer: B

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 # 51
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