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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI for Code and Workflow Optimization		- AI-assisted software development 1. SDLC integration 2. Code generation and debugging - Automation workflows 1. Workflow orchestration concepts 2. API integration basics
Topic 2: Generative AI Models	20%	- Model fundamentals and use cases 1. LLMs and diffusion models overview 2. Use cases: summarization, code generation, content creation - Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - Model hosting and architecture 1. Latency, cost, privacy considerations 2. Cloud vs local deployment trade-offs
Topic 3: Prompt Engineering	15%	- Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting - Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompt design principles 1. Roles, instructions, constraints
Topic 4: AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Topic 5: Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration
Topic 6: Ethics and Security	15%	- Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability - AI security threats 1. Misinformation and hallucinations 2. AI-specific attack surfaces - Data privacy and governance 1. Secure AI data handling

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Cisco AI Technical Practitioner Sample Questions (Q46-Q51):

NEW QUESTION # 46
What happens when the input provided to an LLM exceeds its context window limit?

A. The model caches the overflow data.
B. The model loses the earliest part of the text.
C. The model switches to a different algorithm.
D. The model increases its context window size.

Answer: B

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.

NEW QUESTION # 47
Which action demonstrates the principle of fairness in a generative AI deployment?

A. redacting PII from the training dataset
B. hosting the model in a carbon-neutral data center
C. requiring users to sign Acceptable Use Policies
D. auditing model outputs for bias

Answer: D

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.

NEW QUESTION # 48
What is the role of embeddings in AI systems?

A. Encrypting traffic
B. Assigning IP addresses
C. Compressing logs
D. Representing data numerically in vector space

Answer: D

NEW QUESTION # 49
What is the role of an MCP server in the MCP open standard?

A. to route requests between different AI providers
B. to provide the GUI for the end-user interaction
C. to expose tools and resources to an AI model
D. to host the LLM and perform token inference

Answer: C

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.

NEW QUESTION # 50
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. continuous integration optimization
C. AI-assisted debugging
D. code scaffolding

Answer: C

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 # 51
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