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

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Cisco AI Technical Practitioner Sample Questions (Q18-Q23):

NEW QUESTION # 18
What is the purpose of tokenization in Large Language Models (LLMs)?

A. Routing packets
B. Encrypting passwords
C. Compressing images
D. Splitting text into manageable units

Answer: D

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.

NEW QUESTION # 19
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. AI-assisted debugging
C. continuous integration optimization
D. code scaffolding

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 # 20
How does AI support exploratory data analysis when working with large, unstructured datasets?

A. by tuning inference parameters
B. by identifying patterns and groupings
C. by compressing the datasets
D. by performing bit-by-bit reconstruction

Answer: B

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.

NEW QUESTION # 21
Which activity is an agentic AI use case compared to a standard generative AI use case?

A. autonomously fixing a build
B. translating a short email
C. generating a meeting summary
D. writing a poem about routing

Answer: A

Explanation:
Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.

NEW QUESTION # 22
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

A. parameter count of the model
B. release frequency of the model
C. training duration of the model
D. response creativity level of the model

Answer: A

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.

NEW QUESTION # 23
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