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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering	15%	- Defensive prompting and error mitigation - Advanced prompting techniques - Prompt design principles and best practices
Topic 2: Data Research and Analysis	15%	- Data visualization and insight generation - Data collection, preparation and preprocessing - Data quality, bias and validation
Topic 3: AI Ethics and Security	20%	- AI-specific threats, risks and misinformation - Data privacy and security in AI systems - Responsible AI principles - Compliance and governance frameworks
Topic 4: Generative AI Models	20%	- Context windows, token limits and response management - Model hosting options and trade-offs - Generative AI model families and use cases - Model selection and evaluation in AI hubs
Topic 5: Agentic AI	15%	- Model Context Protocol (MCP) framework - Agentic AI vs Generative AI use cases - Human-in-the-loop strategies - Agent design principles and capabilities
Topic 6: AI for Code and Workflow Optimization	15%	- Integration of AI tools in development processes - Automation and workflow enhancement - AI-assisted code generation and improvement

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Cisco AI Technical Practitioner Sample Questions (Q70-Q75):

NEW QUESTION # 70
What is the benefit of AI Ops (AIOps) in networking?

A. Removes cloud computing
B. Disables telemetry
C. Automates monitoring and incident analysis
D. Eliminates all routers

Answer: C

Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.

NEW QUESTION # 71
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

A. embedding model
B. generative adversarial network
C. multimodal model
D. diffusion model

Answer: C

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.

NEW QUESTION # 72
What is a difference between generative AI and agentic AI?

A. Generative AI produces single-turn responses and agentic AI executes multi-step reasoning loops.
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 operates autonomously and agentic AI requires a prompt for each action.

Answer: A

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 # 73
Which action demonstrates the principle of fairness in a generative AI deployment?

A. redacting PII from the training dataset
B. hosting the model in a carbon-neutral data center
C. auditing model outputs for bias
D. requiring users to sign Acceptable Use Policies

Answer: C

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.

NEW QUESTION # 74
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?

A. automated unit testing
B. continuous integration optimization
C. AI-assisted debugging
D. code scaffolding

Answer: C

Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.

NEW QUESTION # 75
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