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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows
Topic 2: Ethics and Security	15%	- AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - Responsible AI principles 1. Fairness, transparency, accountability 2. Bias mitigation - Data privacy and governance 1. Secure AI data handling
Topic 3: 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
Topic 4: AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Topic 5: Generative AI Models	20%	- Model fundamentals and use cases 1. LLMs and diffusion models overview 2. Use cases: summarization, code generation, content creation - Model hosting and architecture 1. Latency, cost, privacy considerations 2. Cloud vs local deployment trade-offs - Context and retrieval systems 1. RAG, embeddings, vector databases 2. Context windows and token limits
Topic 6: 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

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

NEW QUESTION # 35
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

A. generative adversarial network
B. diffusion model
C. multimodal model
D. embedding model

Answer: C

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.

NEW QUESTION # 36
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 process where the AI model autonomously selects the next step based on its internal confidence score.
C. A dynamic system where branching criteria evolve over time using optimization algorithms.
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 # 37
What does NLP stand for in AI?

A. Neural Link Protocol
B. Natural Language Processing
C. Network Layer Processing
D. Native Logic Programming

Answer: B

Explanation:
NLP enables computers to understand and generate human language.

NEW QUESTION # 38
Which action demonstrates the principle of fairness in a generative AI deployment?

A. hosting the model in a carbon-neutral data center
B. requiring users to sign Acceptable Use Policies
C. redacting PII from the training dataset
D. auditing model outputs for bias

Answer: D

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.

NEW QUESTION # 39
How does a coordinator agent function in a multi-agent AI framework?

A. It serves as the external data source that each agent queries for grounding information.
B. It orchestrates task delegation across specialized agents and aggregates the outputs.
C. It fine-tunes each agent's model weights to optimize performance on assigned tasks.
D. It replaces the need for multiple agents by handling tasks within a single reasoning loop.

Answer: B

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.

NEW QUESTION # 40
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