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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q36-Q41):

NEW QUESTION # 36
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. continuous integration optimization
B. AI-assisted debugging
C. code scaffolding
D. automated unit testing

Answer: B

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 # 37
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?

A. to protect sensitive customer data
B. to increase the speed of AI response
C. to prevent unauthorized access to 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 # 38
In supervised machine learning, what is required?

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

Answer: C

NEW QUESTION # 39
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?

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

Answer: A

Explanation:
Diffusion models generate high-fidelity images by starting from noise and iteratively denoising it over multiple steps until a coherent image is produced.

NEW QUESTION # 40
What is aggregation bias in the context of AI-assisted data analysis?

A. when data is omitted due to incompatible formats
B. when AI uses summary statistics instead of raw data
C. when a summary masks subpatterns
D. when AI fails to combine conflicting patterns

Answer: C

Explanation:
Aggregation bias occurs when summarized or combined data hides meaningful differences across subgroups, causing important subpatterns to be missed.

NEW QUESTION # 41
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