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

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Cisco AI Technical Practitioner Sample Questions (Q31-Q36):

NEW QUESTION # 31
What is the main advantage of cloud-based AI services?

A. Elimination of cybersecurity risks
B. Scalability and flexible compute resources
C. No need for networking
D. Unlimited free hardware

Answer: B

Explanation:
Cloud AI platforms provide scalable infrastructure and elastic computing power for AI workloads.

NEW QUESTION # 32
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

A. parameter count of the model
B. release frequency of the model
C. response creativity level of the model
D. training duration of the model

Answer: A

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.

NEW QUESTION # 33
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

A. describing the situation
B. using few-shot
C. stating the purpose
D. defining a persona

Answer: A

Explanation:
Describing the situation means providing relevant context, background information, and constraints so the AI can generate a response that fits the specific scenario.

NEW QUESTION # 34
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. diffusion models
B. variational autoencoders
C. large language models
D. generative adversarial networks

Answer: A

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 # 35
A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?

A. bias mitigation
B. system safety
C. transparency
D. inclusiveness

Answer: C

Explanation:
Transparency means clearly informing users when AI is being used, what it is doing, and how it may affect their data or experience.

NEW QUESTION # 36
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