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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Generative AI Models	20%	- Model selection 1. Selection from AI hubs 2. Reasoning and multimodal tasks - Major generative model families 1. Diffusion models 2. Large language models (LLMs) - Retrieval Augmented Generation (RAG) 1. Vector databases 2. Grounding AI outputs in external knowledge 3. Embeddings - Common use cases 1. Text summarization 2. Code generation 3. Content creation - Model hosting strategies 1. Cost, latency, privacy, and scalability considerations 2. Cloud-based vs. local deployments - Context windows and token management 1. Token limits 2. Response management
Topic 2: Data Research and Analysis		- Data collection and preparation 1. Data gathering methodologies 2. Data cleaning and preprocessing - Data analysis techniques 1. Data visualization 2. Statistical analysis
Topic 3: Agentic AI	20%	- AI agent design principles 1. Human-in-the-loop (HITL) strategies 2. Orchestration - Model Context Protocol (MCP) 1. Context in agentic AI 2. MCP framework primitives - Differentiation from Generative AI 1. Autonomous capabilities 2. Use cases comparison - Data transformation and mapping 1. Within AI Agents
Topic 4: Prompt Engineering	15%	- Principles and patterns 1. Instructions 2. Constraints 3. Roles - Prompting techniques 1. Iterative prompting 2. Few-shot prompting 3. Chained prompting - Prompt structures 1. Image generation 2. Audio generation 3. Text generation - Security and safety 1. Reducing hallucinations 2. Prompt injection attacks 3. Improving response accuracy 4. Defensive prompting methods
Topic 5: AI for Code and Workflow Optimization		- Workflow automation 1. Process automation 2. Task optimization - AI-assisted code development 1. Code review and debugging 2. Code generation
Topic 6: AI Ethics and Security		- Security threats and mitigations 1. Model security 2. Data privacy - Ethical considerations in AI 1. Bias and fairness 2. Transparency and explainability

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Cisco AI Technical Practitioner Sample Questions (Q51-Q56):

NEW QUESTION # 51
In an AI-powered automation workflow, what is deterministic branching?

A. A process where the AI model autonomously selects the next step based on its internal confidence score.
B. A dynamic system where branching criteria evolve over time using optimization algorithms.
C. A workflow state that pauses execution until a human manually determines the next step.
D. A decision point where the next step is dictated by specific, pre-programmed rules.

Answer: D

Explanation:
Deterministic branching uses predefined rules or conditions to decide the next workflow step, making the path predictable and repeatable rather than model-driven or manually selected.

NEW QUESTION # 52
A team uses AI to scaffold a new microservice. What does this process involve?

A. testing the final production code
B. generating the initial structure
C. removing redundant logic
D. creating automated deployment scripts

Answer: B

Explanation:
Scaffolding uses AI to generate the initial project structure, boilerplate files, configuration, and basic code layout so the team can start development faster.

NEW QUESTION # 53
What is occurring when an agent converts a CSV list into a Markdown table for a final report?

A. data classification
B. data filtering
C. data quantization
D. data transformation

Answer: D

Explanation:
Data transformation changes data from one format or structure into another, such as converting CSV rows into a Markdown table for reporting.

NEW QUESTION # 54
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

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

Answer: C

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 # 55
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

A. Tokenization increases cost by requiring additional GPU run time.
B. Tokenization has no impact because pricing is based on a flat-rate subscription.
C. The number of tokens determines the per-request cost.
D. Costs are determined by the number of API calls.

Answer: C

Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.

NEW QUESTION # 56
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