810-110

810-110日本語版テキスト内容、810-110日本語的中対策

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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
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
AI Ethics and Security	20%	- Data privacy and security in AI systems - Responsible AI principles - Compliance and governance frameworks - AI-specific threats, risks and misinformation
Data Research and Analysis	15%	- Data collection, preparation and preprocessing - Data quality, bias and validation - Data visualization and insight generation
Prompt Engineering	15%	- Advanced prompting techniques - Defensive prompting and error mitigation - Prompt design principles and best practices
AI for Code and Workflow Optimization	15%	- AI-assisted code generation and improvement - Integration of AI tools in development processes - Automation and workflow enhancement
Agentic AI	15%	- Human-in-the-loop strategies - Model Context Protocol (MCP) framework - Agentic AI vs Generative AI use cases - Agent design principles and capabilities

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{CorpName } 810-110日本語版テキスト内容: 大人気問題810-110日本語的中対策

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Cisco AI Technical Practitioner 認定 810-110 試験問題 (Q27-Q32):

質問 # 27
A company informs its users that an AI model is being used to summarize their support tickets. Which responsible AI principle does this practice support?

A. Transparency
B. Bias mitigation
C. Inclusiveness
D. System safety

正解：A

解説：
Transparency is a responsible AI principle requiring organizations to communicate relevant information about how AI systems are used, what purposes they serve, and where automated processing influences user experiences or business operations. Option C is correct because notifying users that AI summarizes their support tickets makes the use of automated processing explicit.
Transparency helps users understand that information they provide may be processed by an AI system rather than exclusively reviewed by human personnel.
For example, a customer support platform may disclose that it uses generative AI to summarize incident descriptions, identify recurring issues, and assist support engineers with case handling.
Such disclosure supports informed engagement and reinforces accountability.
Option A is incorrect because inclusiveness focuses on designing systems that accommodate diverse populations, abilities, and accessibility requirements.
Option B is incorrect because bias mitigation concerns identifying and reducing systematically unfair model behavior.
Option D is incorrect because system safety addresses preventing harmful outputs, failures, or operational consequences.
Transparency alone does not guarantee privacy, fairness, or accuracy. Organizations should also explain relevant limitations, establish appropriate data handling controls, and provide channels for users to challenge problematic outcomes.
Specific Cisco AITECH Course Guide References: Generative AI Security and Privacy Fundamentals; Exam Objective 3.1 - Responsible AI principles, including transparency, accountability, fairness, and safety; Objective 3.4 - AI governance.
Cisco Learning Content

質問 # 28
What is the reason that assigning a persona is regarded as a technical best practice in prompt engineering?

A. It reduces the total token usage.
B. It provides a pattern to emulate.
C. It guides the tone and vocabulary.
D. It provides instructions for desired action.

正解：C

解説：
Persona assignment is a prompt engineering technique that establishes a particular role, professional perspective, communication style, or intended audience for a generative AI model. Option D is correct because assigning a persona helps guide the terminology, tone, vocabulary, and explanatory depth of generated responses.
For example, a practitioner might instruct an AI model to respond as an experienced Cisco network architect explaining a routing problem to junior engineers.
This role encourages the model to use relevant networking terminology, present structured technical explanations, and communicate at an appropriate level of expertise.
Similarly, assigning the persona of a compliance auditor can encourage a more formal, evidence-oriented style when evaluating governance documentation.
Option A is incorrect because explicitly describing the desired action belongs primarily to task instructions rather than persona definition.
Option B is incorrect because assigning a persona does not inherently reduce token consumption. Role descriptions may increase prompt length.
Option C is less accurate because supplying a pattern to emulate is more directly associated with example- based prompting, especially few-shot prompting.
Persona prompting influences how a model frames its response, but it does not establish actual professional qualifications or guarantee technical correctness.
Effective prompts combine role information with clear objectives, contextual details, constraints, and output requirements.
Specific Cisco AITECH Course Guide References: Prompt Engineering for Technical Precision; Advanced Prompting Strategies; Exam Objective 2.1 - Prompt engineering principles and patterns, including roles, instructions, and constraints.
Cisco Learning Content

質問 # 29
Drag and drop the use case from the left onto the AI system type on the right.

正解：

解説：

Explanation:
Correct Drag-and-Drop Placement:
Generative AI
Drafting a technical summary of a meeting transcript for content creation.
Producing realistic synthetic datasets to validate software edge cases during testing.
Agentic AI
Executing a multi-step sequence to autonomously manage database record deletions.
Observing a failed build, analyzing logs, and deciding which diagnostic tool to run next.
Generative AI and Agentic AI represent different approaches to applying artificial intelligence, particularly regarding content creation, autonomous decision-making, and workflow execution.
1. Generative AI - Technical Summary: Generative AI produces new content based on supplied information.
Summarizing meeting transcripts involves processing existing text, identifying important information, and generating a coherent technical summary. The task does not require autonomous execution of external actions.
2. Generative AI - Synthetic Test Data: Generative AI can create realistic artificial datasets to support software testing. These datasets can simulate normal transactions, boundary conditions, and exceptional scenarios without exposing actual customer information.
3. Agentic AI - Database Record Deletion: Agentic AI can coordinate multiple actions toward a defined objective. Managing database deletions may involve identifying qualifying records, validating deletion criteria, invoking database tools, and verifying results. Production deletions should require appropriate human authorization.
4. Agentic AI - Build Troubleshooting: An agentic system can observe a failed software build, analyze diagnostic logs, select troubleshooting tools, and adapt its next action based on execution results. This demonstrates iterative, goal-directed problem-solving.
The fundamental distinction is that Generative AI primarily creates content, whereas Agentic AI coordinates decisions and actions to accomplish operational goals.
Specific Cisco AITECH Course Guide References:
* Generative AI Ecosystem
* AI-Driven Software Engineering
* AI-Powered Workflows and Agentic AI
* Exam Objective 6.1: Differentiate Agentic AI from Generative AI use cases
* Exam Objective 6.2: AI agent design principles, autonomous capabilities, and orchestration
* Exam Objective 5.1: AI across the software development lifecycle
Cisco's published AITECH blueprint explicitly covers these distinctions.

質問 # 30
When using AI to collect external information for a technical report, what professional practice is required for attributing sources?

A. Request a URL for every claim, then verify the existence and accuracy of the source.
B. Utilize only the specific sources that the AI identifies as highly reliable in the chat.
C. Trust the AI's internal knowledge base, as it is trained on vast amounts of data.
D. Enable the AI web search tool to automatically filter out all unverified external sources.

正解：A

解説：
Professional source attribution requires connecting factual claims to identifiable, relevant, and independently verifiable references. Option C is correct because requesting source URLs and checking their authenticity and supporting content reduces the risk of incorporating fabricated citations or unsupported statements into technical reports.
Generative AI systems can produce plausible references that do not exist or that fail to substantiate the accompanying claims.
For example, an AI research assistant might attribute a networking requirement to a particular technical standard, but the cited document may describe a different technology or specification.
The practitioner must therefore validate the reference, inspect the relevant passage, and confirm that the claim accurately represents the source.
Option A is incorrect because automated source filtering cannot guarantee that every retained reference is authoritative or accurate.
Option B is incorrect because extensive training data does not guarantee factual correctness, currency, or source traceability.
Option D is incorrect because model-generated assessments of source reliability should not replace independent verification.
A rigorous research process prioritizes official documentation, current technical standards, original research, and traceable evidence.
The quality of attribution depends on verifying both the source's existence and its actual support for the reported conclusion.
Specific Cisco AITECH Course Guide References: AI-Powered Discovery and Synthesis; Debugging and Correcting AI-Generated Outputs; Objective 4.4 - AI-assisted research and content drafting; Objective 2.4
- Mitigation of AI-generated errors.

質問 # 31
A developer is creating a chatbot that must retain an extensive conversation history. As the conversation continues, the developer observes that the AI "forgets" instructions supplied at the start. What is the most likely reason for this behavior?

A. The conversation has exceeded the model's context window limit.
B. The temperature setting is too high.
C. The API key has expired.
D. The model has reached its knowledge cut-off date.

正解：A

解説：
A context window defines the amount of information, measured in tokens, that a language model can process within a single inference request. Option A is correct because an extensive conversation may exceed the application's supported context capacity, causing earlier instructions to be omitted or summarized.
Many conversational AI applications preserve continuity by resubmitting selected conversation history with each new user message.
As the history grows, the application may truncate older messages to remain within the model's token limit.
Consequently, the model may no longer receive the original instructions and may appear to have forgotten them.
Option B is incorrect as the most likely explanation because higher temperature can increase output variability but does not directly cause context-window overflow.
Option C is incorrect because an expired API key generally causes authentication or request failures rather than selective loss of earlier conversation information.
Option D is incorrect because knowledge cut-off concerns the model's training information, not retention of conversation instructions.
A related issue is reduced attention to particular information within long contexts, even when the formal limit has not been exceeded.
Mitigations include preserving critical instructions, summarizing older exchanges, and retrieving relevant historical context when needed.
Specific Cisco AITECH Course Guide References: Generative AI Ecosystem; AI Architect's Toolkit; Objective 1.3 - Context windows and response management; Objective 5.4 - Context management and output quality.

質問 # 32
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