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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic AI	15%	- Agentic AI vs Generative AI use cases - Human-in-the-loop strategies - Agent design principles and capabilities - Model Context Protocol (MCP) framework
Topic 2: AI for Code and Workflow Optimization	15%	- AI-assisted code generation and improvement - Automation and workflow enhancement - Integration of AI tools in development processes
Topic 3: AI Ethics and Security	20%	- Data privacy and security in AI systems - AI-specific threats, risks and misinformation - Responsible AI principles - Compliance and governance frameworks
Topic 4: Generative AI Models	20%	- Generative AI model families and use cases - Model selection and evaluation in AI hubs - Model hosting options and trade-offs - Context windows, token limits and response management
Topic 5: Prompt Engineering	15%	- Prompt design principles and best practices - Advanced prompting techniques - Defensive prompting and error mitigation
Topic 6: Data Research and Analysis	15%	- Data visualization and insight generation - Data quality, bias and validation - Data collection, preparation and preprocessing

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Cisco AI Technical Practitioner Sample Questions (Q53-Q58):

NEW QUESTION # 53
Which AI-assisted task standardizes inconsistent date formats (e.g., MM/DD/YY and DD-MM- YYYY)?

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

Answer: D

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

NEW QUESTION # 54
Which algorithm category is used when no labels exist in data?

A. Reinforcement learning only
B. Supervised learning
C. Unsupervised learning
D. Deterministic routing

Answer: C

Explanation:
Unsupervised learning discovers patterns or groupings without labeled outputs.

NEW QUESTION # 55
Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?

A. large language models
B. variational autoencoders
C. generative adversarial networks
D. diffusion models

Answer: A

Explanation:
Large language models are designed to process and generate natural language, making them suitable for identifying recurring technical themes and summarizing large volumes of unstructured incident reports.

NEW QUESTION # 56
What happens when the input provided to an LLM exceeds its context window limit?

A. The model switches to a different algorithm.
B. The model caches the overflow data.
C. The model increases its context window size.
D. The model loses the earliest part of the text.

Answer: D

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.

NEW QUESTION # 57
Which AI model type is best suited for image recognition tasks?

A. Decision Tree only
B. Linear Regression
C. Convolutional Neural Network (CNN)
D. K-Means

Answer: C

Explanation:
CNNs are optimized for image and visual pattern recognition.

NEW QUESTION # 58
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