Cisco 810-110資格参考書、810-110予想試験

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Cisco 810-110 Exam Syllabus Topics:

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

>> Cisco 810-110資格参考書 <<

有難い810-110資格参考書試験-試験の準備方法-素敵な810-110予想試験

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Cisco AI Technical Practitioner 認定 810-110 試験問題 (Q60-Q65):

質問 # 60
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

A. to reduce the token count of the agent's system prompt
B. to encrypt the payload before it is sent to the external service
C. to map relationships between multiple tools in an agent workflow
D. to define the required parameters and data types the agent must return

正解：D

解説：
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.

質問 # 61
Which role does AI play in assisting with the refactoring of software code?

A. creating a new database schema
B. integrating a new API
C. identifying flaws and improving design
D. reducing manual code documentation

正解：C

解説：
AI-assisted refactoring helps analyze existing code to identify design flaws, simplify structure, improve readability, and maintain behavior while making the code easier to maintain.

質問 # 62
In an AI-powered automation workflow, what is deterministic branching?

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

正解：D

解説：
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.

質問 # 63
In supervised machine learning, what is required?

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

正解：A

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

質問 # 64
Which algorithm category is used when no labels exist in data?

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

正解：D

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

質問 # 65
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