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Cisco 810-110 Exam Syllabus Topics:

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

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

NEW QUESTION # 11
What is the purpose of a vector database in a RAG architecture?

A. to allocate resources for response generation
B. to perform model weight updates
C. to store numerical representations of data
D. to manage the context window

Answer: C

Explanation:
A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.

NEW QUESTION # 12
Why are embeddings essential to the retrieval phase of a RAG system?

A. They provide a keyword index.
B. They reduce model hallucination.
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 # 13
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

A. to reduce the token count of the agent's system prompt
B. to define the required parameters and data types the agent must return
C. to map relationships between multiple tools in an agent workflow
D. to encrypt the payload before it is sent to the external service

Answer: B

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.

NEW QUESTION # 14
What is a corporate data privacy risk when using a public AI chatbot?

A. The AI will provide hallucinated technical specifications.
B. The data will be encrypted at rest instead of in transit.
C. The AI will provide biased technical advice.
D. The input data may be used for model training.

Answer: D

Explanation:
A public AI chatbot may retain submitted corporate data or use it to improve model training, which can expose confidential, proprietary, or regulated information outside the organization's control.

NEW QUESTION # 15
Which AI model type is best suited for image recognition tasks?

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

Answer: A

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

NEW QUESTION # 16
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