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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 - AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - 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
Prompt Engineering	15%	- Prompt design principles 1. Roles, instructions, constraints - Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompting techniques 1. Chained and iterative prompting 2. Few-shot prompting
AI for Code and Workflow Optimization		- Automation workflows 1. Workflow orchestration concepts 2. API integration basics - AI-assisted software development 1. Code generation and debugging 2. SDLC integration
Generative AI Models	20%	- Model hosting and architecture 1. Latency, cost, privacy considerations 2. Cloud vs local deployment trade-offs - Context and retrieval systems 1. RAG, embeddings, vector databases 2. Context windows and token limits - Model fundamentals and use cases 1. Use cases: summarization, code generation, content creation 2. LLMs and diffusion models overview
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 (Q14-Q19):

NEW QUESTION # 14
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?

A. large language models
B. generative adversarial networks
C. diffusion models
D. variational autoencoders

Answer: C

Explanation:
Diffusion models generate high-fidelity images by starting from noise and iteratively denoising it over multiple steps until a coherent image is produced.

NEW QUESTION # 15
How does a coordinator agent function in a multi-agent AI framework?

A. It orchestrates task delegation across specialized agents and aggregates the outputs.
B. It replaces the need for multiple agents by handling tasks within a single reasoning loop.
C. It serves as the external data source that each agent queries for grounding information.
D. It fine-tunes each agent's model weights to optimize performance on assigned tasks.

Answer: A

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.

NEW QUESTION # 16
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 # 17
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

A. using a short system prompt
B. removing negative constraints
C. increasing the model creativity
D. requesting citations for the summary

Answer: D

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.

NEW QUESTION # 18
Which protocol is commonly used for streaming telemetry in modern networks?

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

Answer: C

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

NEW QUESTION # 19
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