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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q32-Q37):

NEW QUESTION # 32
What is the role of the client in the Model Context Protocol?

A. to act as a tool or service provider
B. to orchestrate interactions with servers
C. to provide resources for task execution
D. to act as an LLM for inferencing

Answer: B

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.

NEW QUESTION # 33
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

A. hypothesize
B. orient
C. correlate
D. visualize

Answer: A

Explanation:
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.

NEW QUESTION # 34
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

A. to provide administrative credentials required to bypass MFA requirements
B. to allow the AI agent to automatically resolve transaction deadlocks
C. to allow the AI agent to bypass the integrity constraints of the database
D. to serve as a safety gate against unintended data modifications

Answer: D

Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.

NEW QUESTION # 35
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

A. elimination of per-token operational costs
B. rapid elastic scaling for unpredictable inference demand
C. lower network latency for all end users
D. full control over data residency and privacy

Answer: B

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.

NEW QUESTION # 36
What is hallucination in generative AI?

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

Answer: B

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

NEW QUESTION # 37
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