Exam 810-110 Learning, Exam 810-110 Question

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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q48-Q53):

NEW QUESTION # 48
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. diffusion models
D. generative adversarial networks

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 # 49
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?

A. to verify the output reliability
B. to increase the response speed
C. to bypass the context window
D. to increase the model creativity

Answer: A

Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.

NEW QUESTION # 50
What is aggregation bias in the context of AI-assisted data analysis?

A. when AI uses summary statistics instead of raw data
B. when a summary masks subpatterns
C. when data is omitted due to incompatible formats
D. when AI fails to combine conflicting patterns

Answer: B

Explanation:
Aggregation bias occurs when summarized or combined data hides meaningful differences across subgroups, causing important subpatterns to be missed.

NEW QUESTION # 51
What is a corporate data privacy risk when using a public AI chatbot?

A. The data will be encrypted at rest instead of in transit.
B. The AI will provide biased technical advice.
C. The AI will provide hallucinated technical specifications.
D. The input data may be used for model training.

Answer: D

Explanation:
A public AI chatbot may retain submitted corporate data or use it to improve model training, which can expose confidential, proprietary, or regulated information outside the organization's control.

NEW QUESTION # 52
Which approach protects PII when utilizing a public AI service for data analysis?

A. utilizing system prompts to instruct the AI to ignore sensitive data fields
B. encrypting the data payload during transit to the AI service provider
C. anonymizing or redacting PH from the dataset before transmitting the request
D. ensuring the AI provider maintains industry-standard SOC 2 compliance

Answer: C

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.

NEW QUESTION # 53
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