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Cisco 810-110 Exam Syllabus Topics:

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

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810-110更新版 & 810-110テスト参考書

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Cisco AI Technical Practitioner 認定 810-110 試験問題 (Q80-Q85):

質問 # 80
Why are embeddings essential to the retrieval phase of a RAG system?

A. They provide a keyword index.
B. They compress the prompt length.
C. They represent semantic meaning.
D. They reduce model hallucination.

正解：C

解説：
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.

質問 # 81
What occurs when the amount of input text supplied to a large language model goes beyond the size of its context window?

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

正解：B

解説：
A context window represents the maximum number of tokens that a large language model can process within a single inference context, subject to the model's architecture and deployment configuration. Option B is the intended answer because many conversational AI systems manage excessive context by truncating older information to preserve more recent messages.
For example, when a conversation becomes longer than the permitted context window, a sliding-window truncation mechanism may remove the earliest messages, causing the model to lose access to previously supplied details.
This can affect response accuracy, continuity, and the model's ability to follow instructions or reference information from earlier interactions.
Option A is incorrect because a model cannot automatically expand its architectural context capacity simply because the input exceeds its limit.
Option C is incorrect because overflow information is not inherently cached for later processing. Prompt caching and context management are distinct mechanisms.
Option D is incorrect because exceeding token capacity does not automatically trigger a change to another inference algorithm.
An important implementation distinction is that truncation is not universal. Some APIs reject oversized requests, while others summarize earlier context or apply configurable truncation strategies.
Therefore, Option B accurately represents a common context-management behavior rather than a mandatory characteristic of every LLM.
Specific Cisco AITECH Course Guide References:
* Generative AI Ecosystem
* AI Architect's Toolkit
* Exam Objective 1.3: Context windows, token limits, and response management
* Exam Objective 5.4: Context-window management and output quality
Cisco includes context-window management as a fundamental learning objective. AWS documentation independently confirms sliding-window truncation and alternative context-management strategies.
Cisco Learning Content+2

質問 # 82
What is the main advantage of cloud-based AI services?

A. Elimination of cybersecurity risks
B. Scalability and flexible compute resources
C. No need for networking
D. Unlimited free hardware

正解：B

解説：
Cloud AI platforms provide scalable infrastructure and elastic computing power for AI workloads.

質問 # 83
What is the role of embeddings in AI systems?

A. Assigning IP addresses
B. Compressing logs
C. Encrypting traffic
D. Representing data numerically in vector space

正解：D

解説：
Embeddings convert text or objects into vectors that capture semantic meaning.

質問 # 84
Which workflow mechanism is being used when a company implements a policy where an AI agent can draft a customer email, but a human must authorize the transmission?

A. data lineage
B. injection mitigation
C. drift monitoring
D. approval gate

正解：D

解説：
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.

質問 # 85
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