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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering	15%	- Prompt design principles 1. Roles, instructions, constraints - Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting - Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks
Ethics and Security	15%	- Data privacy and governance 1. Secure AI data handling - AI security threats 1. Misinformation and hallucinations 2. AI-specific attack surfaces - Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability
AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Generative AI Models	20%	- Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - Model fundamentals and use cases 1. LLMs and diffusion models overview 2. Use cases: summarization, code generation, content creation - Model hosting and architecture 1. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations
AI for Code and Workflow Optimization		- Automation workflows 1. Workflow orchestration concepts 2. API integration basics - AI-assisted software development 1. SDLC integration 2. Code generation and debugging
Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows

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Cisco AI Technical Practitioner Sample Questions (Q73-Q78):

NEW QUESTION # 73
What is the role of the client in the Model Context Protocol?

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

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 # 74
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?

A. gradient inversion
B. direct prompt injection
C. algorithmic bias
D. data poisoning

Answer: D

Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.

NEW QUESTION # 75
What is occurring when an agent converts a CSV list into a Markdown table for a final report?

A. data quantization
B. data transformation
C. data classification
D. data filtering

Answer: B

Explanation:
Data transformation changes data from one format or structure into another, such as converting CSV rows into a Markdown table for reporting.

NEW QUESTION # 76
What is the purpose of providing a JSON schema when configuring tool use for an AI agent?

A. To encrypt the payload before it is sent to the external service
B. To reduce the token count of the agent's system prompt
C. To define the required parameters and data types the agent must return
D. To map relationships between multiple tools in an agent workflow

Answer: C

Explanation:
A JSON schema provides a structured, machine-readable definition of the data fields, parameter types, required properties, and validation constraints associated with an AI agent's tool interface. Option A is correct because it describes the schema's essential role in establishing predictable structured data exchanges.
More precisely, when configuring tool calling, a JSON schema commonly defines the arguments an agent must supply when invoking an external function. The phrase "must return" in Option A should therefore be understood as supplying structured tool-call parameters, rather than necessarily generating the agent's final response.
For example, an agent invoking a network monitoring API might require a device_id string, a metric_type string, and an optional time_range object. JSON schema can specify these properties, identify mandatory fields, and enforce permitted data types.
This improves interoperability, reduces malformed function calls, and enables validation before external operations execute.
Option B is incorrect because encryption requires cryptographic mechanisms such as TLS or application-level encryption. JSON schema provides structural validation, not confidentiality.
Option C is incorrect because schemas introduce descriptive tokens and do not inherently reduce prompt length.
Option D is incorrect because relationships and dependencies between tools are handled through orchestration logic, workflow definitions, or agent planning mechanisms.
Structured tool interfaces are foundational to dependable AI agent integration with APIs and external systems.
Specific Cisco AITECH Course Guide References:
* AI Systems Integration with APIs
* AI-Powered Workflows and Agentic AI
* Exam Objective 6.2: AI agent design principles, autonomous capabilities, and orchestration
* Exam Objective 6.3: Model Context Protocol framework primitives
These objectives establish the official curriculum scope for structured agent-tool integration.

NEW QUESTION # 77
What role does AI play when it comes to assisting with refactoring software code?

A. Integrating a new API
B. Reducing manual code documentation
C. Creating a new database schema
D. Identifying flaws and improving design

Answer: D

NEW QUESTION # 78
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