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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI for Code and Workflow Optimization		- AI-assisted software development 1. SDLC integration 2. Code generation and debugging - Automation workflows 1. API integration basics 2. Workflow orchestration concepts
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. Chained and iterative prompting 2. Few-shot prompting
Topic 3: Generative AI Models	20%	- Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - Model hosting and architecture 1. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations - Model fundamentals and use cases 1. Use cases: summarization, code generation, content creation 2. LLMs and diffusion models overview
Topic 4: Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration
Topic 5: AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Topic 6: 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

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Cisco AI Technical Practitioner Sample Questions (Q56-Q61):

NEW QUESTION # 56
What is a difference between generative AI and agentic AI?

A. Generative AI produces single-turn responses and agentic AI executes multi-step reasoning loops.
B. Generative AI requires tool access and agentic AI relies on its training data.
C. Generative AI uses retrieval-augmented generation and agentic AI does not support external data sources.
D. Generative AI operates autonomously and agentic AI requires a prompt for each action.

Answer: A

Explanation:
Generative AI primarily creates content or responses from a prompt, while agentic AI can plan, reason, use tools, and execute multi-step workflows toward a goal.

NEW QUESTION # 57
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

A. visualize
B. correlate
C. hypothesize
D. orient

Answer: C

Explanation:
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.

NEW QUESTION # 58
Which metric is commonly used to evaluate classification models?

A. Throughput only
B. Accuracy
C. Voltage
D. Packet TTL

Answer: B

Explanation:
Accuracy measures how many predictions were correct out of total predictions.

NEW QUESTION # 59
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

A. expanding the total context window
B. increasing the model sampling rate
C. minimizing the input prompt size
D. increasing the model temperature

Answer: C

Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.

NEW QUESTION # 60
In supervised machine learning, what is required?

A. Random hardware failures
B. Only encrypted traffic
C. No data
D. Labeled datasets

Answer: D

Explanation:
Supervised learning trains models using labeled input-output pairs.

NEW QUESTION # 61
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