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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q15-Q20):

NEW QUESTION # 15
What is the purpose of tokenization in Large Language Models (LLMs)?

A. Routing packets
B. Compressing images
C. Encrypting passwords
D. Splitting text into manageable units

Answer: D

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.

NEW QUESTION # 16
A team uses AI to scaffold a new microservice. What does this process involve?

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

Answer: C

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 # 17
What is fine-tuning in generative AI?

A. Replacing CPUs
B. Formatting storage drives
C. Encrypting prompts
D. Training a pre-trained model on specialized data

Answer: D

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.

NEW QUESTION # 18
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?

A. to protect sensitive customer data
B. to prevent unauthorized access to data
C. to ensure the validity of AI decisions
D. to increase the speed of AI response

Answer: C

Explanation:
Human-in-the-Loop governance ensures that a qualified person reviews, validates, and can override AI-generated decisions when outcomes may significantly affect people, such as medical diagnoses, treatment decisions, credit approvals, or financial risk assessments.

NEW QUESTION # 19
What is the role of embeddings in AI systems?

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

Answer: A

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

NEW QUESTION # 20
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