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

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Cisco AI Technical Practitioner Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which approach protects PII when utilizing a public AI service for data analysis?

A. utilizing system prompts to instruct the AI to ignore sensitive data fields
B. anonymizing or redacting PH from the dataset before transmitting the request
C. ensuring the AI provider maintains industry-standard SOC 2 compliance
D. encrypting the data payload during transit to the AI service provider

Answer: B

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.

NEW QUESTION # 36
Which concept helps reduce overfitting in machine learning?

A. Disabling validation
B. Increasing random noise only
C. Regularization
D. Ignoring datasets

Answer: C

Explanation:
Regularization techniques help models generalize better and avoid memorizing training data.

NEW QUESTION # 37
What is fine-tuning in generative AI?

A. Formatting storage drives
B. Replacing CPUs
C. Encrypting prompts
D. Training a pre-trained model on specialized data

Answer: D

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.

NEW QUESTION # 38
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 # 39
Why is defining a persona considered a best practice for technical prompt engineering?

A. It provides a pattern to emulate.
B. It reduces the total token usage.
C. It guides the tone and vocabulary.
D. It provides instructions for desired action.

Answer: C

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.

NEW QUESTION # 40
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