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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
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
Agentic AI	20%	- Model Context Protocol (MCP) 1. MCP framework primitives 2. Context in agentic AI - Differentiation from Generative AI 1. Use cases comparison 2. Autonomous capabilities - Data transformation and mapping 1. Within AI Agents - AI agent design principles 1. Human-in-the-loop (HITL) strategies 2. Orchestration
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. 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 - Context windows and token management 1. Response management 2. Token limits - Major generative model families 1. Diffusion models 2. Large language models (LLMs)
Prompt Engineering	15%	- Prompt structures 1. Text generation 2. Image generation 3. Audio generation - Security and safety 1. Improving response accuracy 2. Defensive prompting methods 3. Reducing hallucinations 4. Prompt injection attacks - Prompting techniques 1. Chained prompting 2. Few-shot prompting 3. Iterative prompting - Principles and patterns 1. Constraints 2. Instructions 3. Roles
Data Research and Analysis		- Data collection and preparation 1. Data cleaning and preprocessing 2. Data gathering methodologies - Data analysis techniques 1. Data visualization 2. Statistical analysis
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

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Cisco AI Technical Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

A. local LLM deployment on internal hardware
B. managed SaaS AI platform with SOC 2 audit
C. cloud-based API with Zero Data Retention
D. hybrid deployment with cloud-based backup

Answer: A

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.

NEW QUESTION # 39
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?

A. generative adversarial networks
B. variational autoencoders
C. diffusion models
D. large language models

Answer: C

Explanation:
Diffusion models generate high-fidelity images by starting from noise and iteratively denoising it over multiple steps until a coherent image is produced.

NEW QUESTION # 40
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

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

Answer: C

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 # 41
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

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

Answer: A

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 # 42
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?

A. data poisoning
B. direct prompt injection
C. algorithmic bias
D. gradient inversion

Answer: A

Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.

NEW QUESTION # 43
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