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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
AI for Code and Workflow Optimization		- AI-assisted software development 1. Code generation and debugging 2. SDLC integration - Automation workflows 1. API integration basics 2. Workflow orchestration concepts
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
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
Ethics and Security	15%	- Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability - Data privacy and governance 1. Secure AI data handling - AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations
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 (Q28-Q33):

NEW QUESTION # 28
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?

A. to prevent unauthorized access to data
B. to ensure the validity of AI decisions
C. to protect sensitive customer data
D. to increase the speed of AI response

Answer: B

Explanation:
Human-in-the-Loop governance ensures that a qualified person reviews, validates, and can override AI-generated decisions when outcomes may significantly affect people, such as medical diagnoses, treatment decisions, credit approvals, or financial risk assessments.

NEW QUESTION # 29
Which approach protects PII when utilizing a public AI service for data analysis?

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

Answer: D

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 # 30
How does AI support exploratory data analysis when working with large, unstructured datasets?

A. by identifying patterns and groupings
B. by tuning inference parameters
C. by performing bit-by-bit reconstruction
D. by compressing the datasets

Answer: A

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.

NEW QUESTION # 31
What happens when the input provided to an LLM exceeds its context window limit?

A. The model switches to a different algorithm.
B. The model increases its context window size.
C. The model loses the earliest part of the text.
D. The model caches the overflow data.

Answer: C

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.

NEW QUESTION # 32
What is the role of embeddings in AI systems?

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

Answer: C

NEW QUESTION # 33
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