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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ethics and Security	15%	- AI security threats 1. Misinformation and hallucinations 2. AI-specific attack surfaces - Data privacy and governance 1. Secure AI data handling - Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability
Topic 2: Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows
Topic 3: AI for Code and Workflow Optimization		- Automation workflows 1. Workflow orchestration concepts 2. API integration basics - AI-assisted software development 1. Code generation and debugging 2. SDLC integration
Topic 4: AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Topic 5: Generative AI Models	20%	- Model hosting and architecture 1. Latency, cost, privacy considerations 2. Cloud vs local deployment trade-offs - Model fundamentals and use cases 1. Use cases: summarization, code generation, content creation 2. LLMs and diffusion models overview - Context and retrieval systems 1. RAG, embeddings, vector databases 2. Context windows and token limits
Topic 6: Prompt Engineering	15%	- Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting - Prompt design principles 1. Roles, instructions, constraints - Security and robustness 1. Prompt injection risks 2. Mitigation and defensive prompting

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Cisco AI Technical Practitioner Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which Cisco solution helps automate network operations using AI insights?

A. Cisco BIOS Manager
B. Cisco Hub
C. Cisco Repeater Utility
D. Cisco DNA Center

Answer: D

Explanation:
Cisco DNA Center uses AI and analytics for intent-based networking and automation.

NEW QUESTION # 29
How does AI support exploratory data analysis when working with large, unstructured datasets?

A. by identifying patterns and groupings
B. by compressing the datasets
C. by tuning inference parameters
D. by performing bit-by-bit reconstruction

Answer: A

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.

NEW QUESTION # 30
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

A. It maintains contextual alignment.
B. It ensures the most creative text.
C. It allows for easy repurposing.
D. It minimizes token consumption.

Answer: C

Explanation:
A format-agnostic strategy keeps the content independent of a specific final layout, making it easier to reuse or adapt the same proposal material for different formats such as email, slides, documents, or reports.

NEW QUESTION # 31
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

A. Replace neural networks
B. Encrypt AI datasets
C. Reduce GPU usage only
D. Improve model responses with external knowledge

Answer: D

Explanation:
RAG combines a large language model with external data retrieval systems so the model can answer using updated and domain-specific knowledge.

NEW QUESTION # 32
A team uses AI to scaffold a new microservice. What does this process involve?

A. creating automated deployment scripts
B. generating the initial structure
C. testing the final production code
D. removing redundant logic

Answer: B

Explanation:
Scaffolding uses AI to generate the initial project structure, boilerplate files, configuration, and basic code layout so the team can start development faster.

NEW QUESTION # 33
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