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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q55-Q60):

NEW QUESTION # 55
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. generative adversarial networks
B. variational autoencoders
C. diffusion models
D. large language models

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 # 56
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

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

Answer: B

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 # 57
What is the role of embeddings in AI systems?

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

Answer: A

NEW QUESTION # 58
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

A. Encrypt AI datasets
B. Reduce GPU usage only
C. Replace neural networks
D. Improve model responses with external knowledge

Answer: D

Explanation:
RAG combines a large language model with external data retrieval systems so the model can answer using updated and domain-specific knowledge.

NEW QUESTION # 59
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. AI-assisted debugging
B. automated unit testing
C. code scaffolding
D. continuous integration optimization

Answer: A

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 # 60
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