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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Topic 2: Prompt Engineering	15%	- Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompt design principles 1. Roles, instructions, constraints - Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting
Topic 3: Ethics and Security	15%	- Data privacy and governance 1. Secure AI data handling - AI security threats 1. Misinformation and hallucinations 2. AI-specific attack surfaces - Responsible AI principles 1. Fairness, transparency, accountability 2. Bias mitigation
Topic 4: 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
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 - Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - Model hosting and architecture 1. Latency, cost, privacy considerations 2. Cloud vs local deployment trade-offs
Topic 6: 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 (Q67-Q72):

NEW QUESTION # 67
Which AI-assisted task standardizes inconsistent date formats (e.g., MM/DD/YY and DD-MM- YYYY)?

A. data transformation
B. data visualization
C. data correlation
D. thematic summarization

Answer: A

Explanation:
Data transformation converts inconsistent data into a consistent format, such as standardizing multiple date formats into one required representation.

NEW QUESTION # 68
Which action demonstrates the principle of fairness in a generative AI deployment?

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

Answer: C

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 # 69
Which AI concern focuses on protecting sensitive information?

A. Clock synchronization
B. Packet fragmentation
C. VLAN tagging
D. Data privacy

Answer: D

Explanation:
AI systems must ensure sensitive data is securely handled and protected.

NEW QUESTION # 70
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 workflow state that pauses execution until a human manually determines the next step.
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 # 71
What is the role of an MCP server in the MCP open standard?

A. to route requests between different AI providers
B. to host the LLM and perform token inference
C. to provide the GUI for the end-user interaction
D. to expose tools and resources to an AI model

Answer: D

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.

NEW QUESTION # 72
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