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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q53-Q58):

NEW QUESTION # 53
Which AI model type is best suited for image recognition tasks?

A. Linear Regression
B. Decision Tree only
C. Convolutional Neural Network (CNN)
D. K-Means

Answer: C

Explanation:
CNNs are optimized for image and visual pattern recognition.

NEW QUESTION # 54
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

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

Answer: B

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 # 55
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 # 56
What is the role of embeddings in AI systems?

A. Compressing logs
B. Encrypting traffic
C. Representing data numerically in vector space
D. Assigning IP addresses

Answer: C

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.

NEW QUESTION # 57
In supervised machine learning, what is required?

A. Random hardware failures
B. Only encrypted traffic
C. Labeled datasets
D. No data

Answer: C

NEW QUESTION # 58
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