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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q13-Q18):

NEW QUESTION # 13
In an AI-powered automation workflow, what is the function of an approval gate?

A. to trigger automated completion notifications to project stakeholders
B. to apply human judgement before high-impact actions are executed
C. to ensure the internal logic of AI remains closed for modification
D. to automatically block deployments if system health alerts are active

Answer: B

Explanation:
An approval gate adds human review before an automated workflow performs a high-impact action, helping ensure that AI-driven decisions are checked for correctness, risk, and business impact before execution.

NEW QUESTION # 14
What is the purpose of a vector database in a RAG architecture?

A. to store numerical representations of data
B. to perform model weight updates
C. to manage the context window
D. to allocate resources for response generation

Answer: A

Explanation:
A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.

NEW QUESTION # 15
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

A. using smaller contexts
B. removing system prompts
C. using known patterns
D. implementing constraints

Answer: D

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.

NEW QUESTION # 16
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 ensure the validity of AI decisions
D. to prevent unauthorized access to data

Answer: C

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 # 17
What is aggregation bias in the context of AI-assisted data analysis?

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

Answer: A

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

NEW QUESTION # 18
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