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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is a difference between generative AI and agentic AI?

A. Generative AI uses retrieval-augmented generation and agentic AI does not support external data sources.
B. Generative AI operates autonomously and agentic AI requires a prompt for each action.
C. Generative AI requires tool access and agentic AI relies on its training data.
D. Generative AI produces single-turn responses and agentic AI executes multi-step reasoning loops.

Answer: D

Explanation:
Generative AI primarily creates content or responses from a prompt, while agentic AI can plan, reason, use tools, and execute multi-step workflows toward a goal.

NEW QUESTION # 12
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

A. Tokenization increases cost by requiring additional GPU run time.
B. Tokenization has no impact because pricing is based on a flat-rate subscription.
C. The number of tokens determines the per-request cost.
D. Costs are determined by the number of API calls.

Answer: C

Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.

NEW QUESTION # 13
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?

A. to increase the response speed
B. to bypass the context window
C. to increase the model creativity
D. to verify the output reliability

Answer: D

Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.

NEW QUESTION # 14
Which AI model type is best suited for image recognition tasks?

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

Answer: C

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

NEW QUESTION # 15
Why are prompts important in generative AI systems?

A. They eliminate hallucinations completely
B. They encrypt AI hardware
C. They replace training datasets
D. They determine model behavior and output quality

Answer: D

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.

NEW QUESTION # 16
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