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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows
Topic 2: Prompt Engineering	15%	- Prompt design principles 1. Roles, instructions, constraints - Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting
Topic 3: 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. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations
Topic 4: AI for Code and Workflow Optimization		- Automation workflows 1. Workflow orchestration concepts 2. API integration basics - AI-assisted software development 1. SDLC integration 2. Code generation and debugging
Topic 5: AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Topic 6: Ethics and Security	15%	- Data privacy and governance 1. Secure AI data handling - AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability

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Cisco AI Technical Practitioner Sample Questions (Q14-Q19):

NEW QUESTION # 14
A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?

A. prompt chaining
B. few-shot prompting
C. meta-prompting
D. chain-of-thought prompting

Answer: A

Explanation:
Prompt chaining breaks a complex task into a sequence of connected prompts, where each response becomes part of the input for the next step.

NEW QUESTION # 15
Which algorithm category is used when no labels exist in data?

A. Unsupervised learning
B. Reinforcement learning only
C. Deterministic routing
D. Supervised learning

Answer: A

Explanation:
Unsupervised learning discovers patterns or groupings without labeled outputs.

NEW QUESTION # 16
How does a coordinator agent function in a multi-agent AI framework?

A. It replaces the need for multiple agents by handling tasks within a single reasoning loop.
B. It serves as the external data source that each agent queries for grounding information.
C. It fine-tunes each agent's model weights to optimize performance on assigned tasks.
D. It orchestrates task delegation across specialized agents and aggregates the outputs.

Answer: D

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.

NEW QUESTION # 17
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

A. maintenance
B. testing
C. implementation
D. requirements

Answer: D

Explanation:
In the requirements phase, AI can analyze stakeholder documentation to identify logical gaps or inconsistencies and help convert business needs into structured user stories.

NEW QUESTION # 18
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 encrypt the payload before it is sent to the external service
C. to map relationships between multiple tools in an agent workflow
D. to define the required parameters and data types the agent must return

Answer: D

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 # 19
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