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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Generative AI Models20%- Context windows, token limits and response management
- Generative AI model families and use cases
- Model selection and evaluation in AI hubs
- Model hosting options and trade-offs
Topic 2: Agentic AI15%- Agent design principles and capabilities
- Model Context Protocol (MCP) framework
- Human-in-the-loop strategies
- Agentic AI vs Generative AI use cases
Topic 3: AI for Code and Workflow Optimization15%- Integration of AI tools in development processes
- Automation and workflow enhancement
- AI-assisted code generation and improvement
Topic 4: Prompt Engineering15%- Advanced prompting techniques
- Prompt design principles and best practices
- Defensive prompting and error mitigation
Topic 5: AI Ethics and Security20%- Data privacy and security in AI systems
- Responsible AI principles
- AI-specific threats, risks and misinformation
- Compliance and governance frameworks
Topic 6: Data Research and Analysis15%- Data quality, bias and validation
- Data collection, preparation and preprocessing
- Data visualization and insight generation

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Cisco AI Technical Practitioner Sample Questions (Q69-Q74):

NEW QUESTION # 69
Which approach protects PII when utilizing a public AI service for data analysis?

Answer: A

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.


NEW QUESTION # 70
An analyst needs to use a model capable of simultaneously analyzing a network topology diagram along with a set of configuration logs. Which type of model should be chosen for this task?

Answer: A


NEW QUESTION # 71
Why are prompts important in generative AI systems?

Answer: C

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.


NEW QUESTION # 72
A team uses AI to scaffold a new microservice. What does this process involve?

Answer: B

Explanation:
AI-assisted scaffolding refers to the automated generation of the initial structure, foundational components, and boilerplate code required to establish a software application or microservice. Option B is correct because scaffolding accelerates software development by creating a consistent project foundation before developers implement application-specific business logic.
In microservice architecture, AI-powered scaffolding tools can generate directory structures, configuration files, API endpoint templates, dependency manifests, application entry points, and basic service interfaces.
For example, a developer can instruct a generative AI coding assistant to initialize a Python FastAPI microservice with predefined REST endpoints, dependency management, and a standard project layout.
This approach reduces repetitive development tasks, promotes architectural consistency, and enables engineers to concentrate on service functionality.
Option A is incorrect because removing redundant logic constitutes code refactoring or optimization rather than scaffolding.
Option C is incorrect because production code testing involves functional verification, integration testing, and quality assurance after implementation.
Option D is incorrect because generating automated deployment scripts belongs primarily to deployment automation and CI/CD processes, although scaffolding tools may optionally generate initial deployment configuration templates.
Cisco's AITECH curriculum emphasizes AI-powered code generation and improving software engineering efficiency throughout the development lifecycle.
Specific Cisco AITECH Course Guide References:
* AI-Driven Software Engineering
* AI Systems Integration with APIs
* Exam domain: AI for Code and Workflow Optimization
These topics appear in Cisco's official AITECH course overview and learning objectives.


NEW QUESTION # 73
What role does AI play when it comes to assisting with refactoring software code?

Answer: B


NEW QUESTION # 74
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