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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q50-Q55):

NEW QUESTION # 50
Why is defining a persona considered a best practice for technical prompt engineering?

A. It guides the tone and vocabulary.
B. It provides instructions for desired action.
C. It reduces the total token usage.
D. It provides a pattern to emulate.

Answer: A

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.

NEW QUESTION # 51
In an AI-powered automation workflow, what is deterministic branching?

A. A decision point where the next step is dictated by specific, pre-programmed rules.
B. A workflow state that pauses execution until a human manually determines the next step.
C. A process where the AI model autonomously selects the next step based on its internal confidence score.
D. A dynamic system where branching criteria evolve over time using optimization algorithms.

Answer: A

Explanation:
Deterministic branching uses predefined rules or conditions to decide the next workflow step, making the path predictable and repeatable rather than model-driven or manually selected.

NEW QUESTION # 52
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

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

Answer: C

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 # 53
Which Cisco AI capability helps detect abnormal network behavior?

A. Static routing
B. AI-driven anomaly detection
C. BIOS recovery mode
D. Manual packet counting

Answer: B

Explanation:
AI systems can identify unusual patterns and potential threats automatically.

NEW QUESTION # 54
What is a 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 define the required parameters and data types the agent must return
C. to reduce the token count of the agent's system prompt
D. to map relationships between multiple tools in an agent workflow

Answer: B

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.

NEW QUESTION # 55
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