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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Topic 2: 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 3: 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 4: Ethics and Security	15%	- Data privacy and governance 1. Secure AI data handling - Responsible AI principles 1. Fairness, transparency, accountability 2. Bias mitigation - AI security threats 1. Misinformation and hallucinations 2. AI-specific attack surfaces
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. 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
Topic 6: Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration

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Cisco AI Technical Practitioner Sample Questions (Q19-Q24):

NEW QUESTION # 19
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?

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

Answer: D

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 # 20
Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?

A. large language models
B. variational autoencoders
C. generative adversarial networks
D. diffusion models

Answer: A

Explanation:
Large language models are designed to process and generate natural language, making them suitable for identifying recurring technical themes and summarizing large volumes of unstructured incident reports.

NEW QUESTION # 21
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

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

Answer: B

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 # 22
In supervised machine learning, what is required?

A. No data
B. Labeled datasets
C. Random hardware failures
D. Only encrypted traffic

Answer: B

NEW QUESTION # 23
How does a coordinator agent function in a multi-agent AI framework?

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

Answer: C

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 # 24
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