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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI for Code and Workflow Optimization		- Automation workflows 1. API integration basics 2. Workflow orchestration concepts - AI-assisted software development 1. SDLC integration 2. Code generation and debugging
Topic 2: AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Topic 3: Ethics and Security	15%	- Responsible AI principles 1. Fairness, transparency, accountability 2. Bias mitigation - Data privacy and governance 1. Secure AI data handling - AI security threats 1. Misinformation and hallucinations 2. AI-specific attack surfaces
Topic 4: Generative AI Models	20%	- Model fundamentals and use cases 1. Use cases: summarization, code generation, content creation 2. LLMs and diffusion models overview - Model hosting and architecture 1. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations - Context and retrieval systems 1. RAG, embeddings, vector databases 2. Context windows and token limits
Topic 5: Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration
Topic 6: Prompt Engineering	15%	- Prompt design principles 1. Roles, instructions, constraints - Security and robustness 1. Prompt injection risks 2. Mitigation and defensive prompting - Prompting techniques 1. Chained and iterative prompting 2. Few-shot prompting

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Cisco AI Technical Practitioner Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which action demonstrates the principle of fairness in a generative AI deployment?

A. hosting the model in a carbon-neutral data center
B. redacting PII from the training dataset
C. requiring users to sign Acceptable Use Policies
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 # 22
Which Cisco AI capability helps detect abnormal network behavior?

A. Manual packet counting
B. BIOS recovery mode
C. AI-driven anomaly detection
D. Static routing

Answer: C

Explanation:
AI systems can identify unusual patterns and potential threats automatically.

NEW QUESTION # 23
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?

A. algorithmic bias
B. data poisoning
C. gradient inversion
D. direct prompt injection

Answer: B

Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.

NEW QUESTION # 24
How does context window management help control the cost of a long-running AI conversation?

A. by increasing the context window size to reduce the total number of API calls
B. by automatically switching to a lower-cost model as the conversation history grows
C. by capping the cumulative number of input tokens sent with each new prompt
D. by switching to an algorithm that reduces the character-to-token ratio

Answer: C

Explanation:
Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.

NEW QUESTION # 25
What is fine-tuning in generative AI?

A. Replacing CPUs
B. Training a pre-trained model on specialized data
C. Encrypting prompts
D. Formatting storage drives

Answer: B

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.

NEW QUESTION # 26
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