Latest Cisco 810-110 of exam practice questions and answers

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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: Agentic AI	15%	- Human-in-the-loop strategies - Model Context Protocol (MCP) framework - Agent design principles and capabilities - Agentic AI vs Generative AI use cases
Topic 3: Prompt Engineering	15%	- Advanced prompting techniques - Defensive prompting and error mitigation - Prompt design principles and best practices
Topic 4: 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 5: Generative AI Models	20%	- Context windows, token limits and response management - Model hosting options and trade-offs - Generative AI model families and use cases - Model selection and evaluation in AI hubs
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 (Q59-Q64):

NEW QUESTION # 59
In an AI-powered automation workflow, what is deterministic branching?

A. A dynamic system where branching criteria evolve over time using optimization algorithms.
B. A process where the AI model autonomously selects the next step based on its internal confidence score.
C. A decision point where the next step is dictated by specific, pre-programmed rules.
D. A workflow state that pauses execution until a human manually determines the next step.

Answer: C

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 # 60
Which metric is commonly used to evaluate classification models?

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

Answer: A

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

NEW QUESTION # 61
What is a use case for generative AI in the testing phase of the software development lifecycle?

A. drafting user stories
B. generating automated documentation
C. generating synthetic test data
D. performing static code analysis

Answer: C

Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.

NEW QUESTION # 62
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

A. using smaller contexts
B. using known patterns
C. implementing constraints
D. removing system prompts

Answer: C

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.

NEW QUESTION # 63
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 increase the model creativity
B. to bypass the context window
C. to verify the output reliability
D. to increase the response speed

Answer: C

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

NEW QUESTION # 64
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