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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q47-Q52):

NEW QUESTION # 47
What is the role of an MCP server in the MCP open standard?

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

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 # 48
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

A. full control over data residency and privacy
B. lower network latency for all end users
C. rapid elastic scaling for unpredictable inference demand
D. elimination of per-token operational costs

Answer: C

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.

NEW QUESTION # 49
Which protocol is commonly used for streaming telemetry in modern networks?

A. ARP
B. FTP
C. gRPC
D. Telnet

Answer: C

Explanation:
gRPC supports efficient real-time telemetry streaming in modern network automation environments.

NEW QUESTION # 50
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

A. diffusion model
B. embedding model
C. generative adversarial network
D. multimodal model

Answer: D

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.

NEW QUESTION # 51
How does context window management help control the cost of a long-running AI conversation?

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

Answer: B

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 # 52
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