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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q72-Q77):

NEW QUESTION # 72
A developer provides AI with three examples of a log entry mapped to a specific error category before asking AI to categorize a new log entry. Which technique is being used?

A. chain-of-thought
B. metaprompting
C. zero-shot prompting
D. few-shot prompting

Answer: D

Explanation:
Few-shot prompting provides several examples of the expected input-output pattern so the AI can apply the same pattern to a new case.

NEW QUESTION # 73
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

A. The number of tokens determines the per-request cost.
B. Costs are determined by the number of API calls.
C. Tokenization increases cost by requiring additional GPU run time.
D. Tokenization has no impact because pricing is based on a flat-rate subscription.

Answer: A

Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.

NEW QUESTION # 74
In an AI-powered automation workflow, what is deterministic branching?

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

Answer: C

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 # 75
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

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

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 # 76
How does context window management help control the cost of a long-running AI conversation?

A. by capping the cumulative number of input tokens sent with each new prompt
B. by automatically switching to a lower-cost model as the conversation history grows
C. by switching to an algorithm that reduces the character-to-token ratio
D. by increasing the context window size to reduce the total number of API calls

Answer: A

Explanation:
Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.

NEW QUESTION # 77
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