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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q30-Q35):

NEW QUESTION # 30
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 ensure the validity of AI decisions
C. to protect sensitive customer data
D. to increase the speed of AI response

Answer: B

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 # 31
What is a difference between generative AI and agentic AI?

A. Generative AI operates autonomously and agentic AI requires a prompt for each action.
B. Generative AI uses retrieval-augmented generation and agentic AI does not support external data sources.
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 # 32
What is hallucination in generative AI?

A. Network congestion
B. AI generating incorrect or fabricated information
C. GPU overheating
D. Database replication

Answer: B

Explanation:
Hallucination occurs when an AI confidently produces false or misleading outputs.

NEW QUESTION # 33
Which metric is commonly used to evaluate classification models?

A. Packet TTL
B. Throughput only
C. Accuracy
D. Voltage

Answer: C

Explanation:
Accuracy measures how many predictions were correct out of total predictions.

NEW QUESTION # 34
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

A. It minimizes token consumption.
B. It maintains contextual alignment.
C. It allows for easy repurposing.
D. It ensures the most creative text.

Answer: C

Explanation:
A format-agnostic strategy keeps the content independent of a specific final layout, making it easier to reuse or adapt the same proposal material for different formats such as email, slides, documents, or reports.

NEW QUESTION # 35
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