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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q60-Q65):

NEW QUESTION # 60
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

A. to provide administrative credentials required to bypass MFA requirements
B. to allow the AI agent to bypass the integrity constraints of the database
C. to allow the AI agent to automatically resolve transaction deadlocks
D. to serve as a safety gate against unintended data modifications

Answer: D

Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.

NEW QUESTION # 61
Which workflow mechanism is being used when a company implements a policy where an AI agent can draft a customer email, but a human must authorize the transmission?

A. approval gate
B. injection mitigation
C. drift monitoring
D. data lineage

Answer: A

Explanation:
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.

NEW QUESTION # 62
In an AI-powered automation workflow, what is deterministic branching?

A. A workflow state that pauses execution until a human manually determines the next step.
B. A dynamic system where branching criteria evolve over time using optimization algorithms.
C. A process where the AI model autonomously selects the next step based on its internal confidence score.
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 # 63
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 # 64
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

A. full control over data residency and privacy
B. rapid elastic scaling for unpredictable inference demand
C. lower network latency for all end users
D. elimination of per-token operational costs

Answer: B

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.

NEW QUESTION # 65
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