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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Generative AI Models	20%	- Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - 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
AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Ethics and Security	15%	- Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability - AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - Data privacy and governance 1. Secure AI data handling
Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration
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
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. Mitigation and defensive prompting 2. Prompt injection risks

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Cisco AI Technical Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

A. cloud-based API with Zero Data Retention
B. managed SaaS AI platform with SOC 2 audit
C. hybrid deployment with cloud-based backup
D. local LLM deployment on internal hardware

Answer: D

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.

NEW QUESTION # 39
Why are embeddings essential to the retrieval phase of a RAG system?

A. They compress the prompt length.
B. They provide a keyword index.
C. They reduce model hallucination.
D. They represent semantic meaning.

Answer: D

Explanation:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.

NEW QUESTION # 40
How does AI support exploratory data analysis when working with large, unstructured datasets?

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

Answer: B

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 # 41
What is the purpose of a vector database in a RAG architecture?

A. to allocate resources for response generation
B. to manage the context window
C. to perform model weight updates
D. to store numerical representations of data

Answer: D

Explanation:
A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.

NEW QUESTION # 42
In supervised machine learning, what is required?

A. Random hardware failures
B. Only encrypted traffic
C. Labeled datasets
D. No data

Answer: C

Explanation:
Supervised learning trains models using labeled input-output pairs.

NEW QUESTION # 43
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