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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. Iterative prompting 3. Chained prompting - Security and safety 1. Prompt injection attacks 2. Reducing hallucinations 3. Improving response accuracy 4. Defensive prompting methods - Principles and patterns 1. Roles 2. Constraints 3. Instructions - Prompt structures 1. Audio generation 2. Image generation 3. Text generation
Generative AI Models	20%	- Context windows and token management 1. Response management 2. Token limits - Major generative model families 1. Diffusion models 2. Large language models (LLMs) - Model selection 1. Reasoning and multimodal tasks 2. Selection from AI hubs - Common use cases 1. Content creation 2. Text summarization 3. Code generation - Model hosting strategies 1. Cloud-based vs. local deployments 2. Cost, latency, privacy, and scalability considerations - Retrieval Augmented Generation (RAG) 1. Grounding AI outputs in external knowledge 2. Embeddings 3. Vector databases
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
AI for Code and Workflow Optimization		- Workflow automation 1. Process automation 2. Task optimization - AI-assisted code development 1. Code review and debugging 2. Code generation
Agentic AI	20%	- AI agent design principles 1. Orchestration 2. Human-in-the-loop (HITL) strategies - Model Context Protocol (MCP) 1. Context in agentic AI 2. MCP framework primitives - Data transformation and mapping 1. Within AI Agents - Differentiation from Generative AI 1. Autonomous capabilities 2. Use cases comparison

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Cisco AI Technical Practitioner Sample Questions (Q55-Q60):

NEW QUESTION # 55
Which concept helps reduce overfitting in machine learning?

A. Increasing random noise only
B. Ignoring datasets
C. Disabling validation
D. Regularization

Answer: D

Explanation:
Regularization techniques help models generalize better and avoid memorizing training data.

NEW QUESTION # 56
Why is defining a persona considered a best practice for technical prompt engineering?

A. It provides a pattern to emulate.
B. It reduces the total token usage.
C. It provides instructions for desired action.
D. It guides the tone and vocabulary.

Answer: D

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.

NEW QUESTION # 57
Which approach protects PII when utilizing a public AI service for data analysis?

A. utilizing system prompts to instruct the AI to ignore sensitive data fields
B. encrypting the data payload during transit to the AI service provider
C. ensuring the AI provider maintains industry-standard SOC 2 compliance
D. anonymizing or redacting PH from the dataset before transmitting the request

Answer: D

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.

NEW QUESTION # 58
What is aggregation bias in the context of AI-assisted data analysis?

A. when AI uses summary statistics instead of raw data
B. when data is omitted due to incompatible formats
C. when a summary masks subpatterns
D. when AI fails to combine conflicting patterns

Answer: C

Explanation:
Aggregation bias occurs when summarized or combined data hides meaningful differences across subgroups, causing important subpatterns to be missed.

NEW QUESTION # 59
Why are embeddings essential to the retrieval phase of a RAG system?

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

Answer: C

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 # 60
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