810-110試験勉強攻略、810-110試験問題

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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Generative AI Models20%- Common use cases
  • 1. Content creation
  • 2. Text summarization
  • 3. Code generation
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Embeddings
  • 3. Grounding AI outputs in external knowledge
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Context windows and token management
  • 1. Response management
  • 2. Token limits
Topic 2: Agentic AI20%- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- Data transformation and mapping
  • 1. Within AI Agents
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
Topic 3: AI Ethics and Security- Security threats and mitigations
  • 1. Model security
  • 2. Data privacy
- Ethical considerations in AI
  • 1. Transparency and explainability
  • 2. Bias and fairness
Topic 4: Prompt Engineering15%- Principles and patterns
  • 1. Instructions
  • 2. Constraints
  • 3. Roles
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Chained prompting
  • 3. Iterative prompting
- Prompt structures
  • 1. Text generation
  • 2. Image generation
  • 3. Audio generation
- Security and safety
  • 1. Improving response accuracy
  • 2. Reducing hallucinations
  • 3. Prompt injection attacks
  • 4. Defensive prompting methods
Topic 5: AI for Code and Workflow Optimization- Workflow automation
  • 1. Task optimization
  • 2. Process automation
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
Topic 6: Data Research and Analysis- Data analysis techniques
  • 1. Data visualization
  • 2. Statistical analysis
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies

>> 810-110試験勉強攻略 <<

試験の準備方法-高品質な810-110試験勉強攻略試験-正確的な810-110試験問題

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Cisco AI Technical Practitioner 認定 810-110 試験問題 (Q82-Q87):

質問 # 82
What is fine-tuning in generative AI?

正解：C

解説：
Fine-tuning adapts a general AI model to specific domains or business use cases.


質問 # 83
An autonomous agent is intended to carry out bulk deletions of old customer records from a production database. Which design pattern should be used to avoid accidental data loss?

正解：B

解説：
Human-in-the-Loop (HITL) is an AI governance and workflow design pattern requiring human review or authorization before selected high-risk actions are executed. Option A is correct because bulk deletion of production customer records can cause irreversible data loss, operational disruption, and potential compliance violations.
In a properly controlled workflow, the autonomous agent first identifies records that meet established deletion criteria.
The agent then generates a proposed deletion plan specifying affected records, selection conditions, expected impact, and recovery options.
Before executing the database operation, the workflow enters an approval state requiring an authorized reviewer to evaluate the proposed changes.
Option B is incorrect because increasing model capability does not eliminate hallucinations, incorrect assumptions, or unauthorized actions.
Option C is incorrect because a larger token budget allows additional processing but does not guarantee more reliable decisions.
Option D is incorrect because unrestricted autonomy increases the potential impact of mistakes when handling sensitive production data.
Additional safeguards should include least-privilege database permissions, transaction controls, pre-deletion backups, deletion thresholds, audit logging, and tested rollback procedures.
HITL provides an explicit authorization boundary that prevents the agent from executing high-impact operations without appropriate oversight.
Specific Cisco AITECH Course Guide References:
* AI-Powered Workflows and Agentic AI
* Generative AI Security and Privacy Fundamentals
* Exam Objective 6.4: Human-in-the-Loop strategies
* Exam Objective 3.4: AI governance, risk management, and compliance


質問 # 84
A practitioner applies the self-consistency technique by running the same prompt three separate times and comparing the resulting outputs. What is the purpose of this approach?

正解：C

解説：
Self-consistency is a reasoning and prompt evaluation technique that examines multiple independently generated candidate responses to identify whether they converge on a common conclusion. Option B is correct because comparing repeated outputs helps assess response stability and provides evidence about the reliability of the model's conclusions.
In the original self-consistency methodology, a model generates multiple reasoning paths for the same problem. The final answer is selected according to the consistency of the conclusions across those paths.
For example, a practitioner evaluating a network capacity calculation might obtain three independently generated solutions and compare their numerical results.
If all responses converge on the same answer, this provides additional consistency evidence, although agreement does not guarantee factual correctness.
Option A is incorrect because generating multiple responses typically increases total inference processing and may increase latency.
Option C is incorrect because repeated sampling does not extend or bypass a model's context-window limitation.
Option D is incorrect because the primary objective is improving reasoning reliability, not increasing creative diversity.
A critical distinction is that true self-consistency involves comparing multiple candidate reasoning paths or answers, not merely repeating a deterministic request that always produces identical output.
For high-stakes technical tasks, consistency checks should be supplemented with authoritative references, independent calculations, or executable tests.
Specific Cisco AITECH Course Guide References:
* Advanced Prompting Strategies
* Debugging and Correcting AI-Generated Outputs
* Exam Objective 2.2: Prompting techniques and advanced prompting patterns
* Exam Objective 2.4: Mitigation strategies for AI-generated errors
The original self-consistency research describes selecting consistent conclusions from multiple reasoning paths, providing additional technical grounding for this answer.


質問 # 85
What makes embeddings a critical component of the retrieval step in a Retrieval-Augmented Generation (RAG) system?

正解：D

解説：
Embeddings are numerical vector representations that encode meaningful characteristics of information, allowing retrieval systems to compare relationships between documents and user queries. Option C is correct because embeddings enable semantic similarity search rather than relying exclusively on exact keyword matching.
In a Retrieval-Augmented Generation architecture, source documents are typically divided into manageable passages. An embedding model converts these passages into vectors that can be stored and indexed in a vector database.
When a user submits a question, the system converts the query into a compatible embedding and retrieves passages with similar semantic representations.
For example, a query about reducing network congestion may retrieve documentation discussing traffic optimization even when the document does not contain the exact wording of the question.
Option A is incorrect because embeddings do not inherently compress the textual prompt supplied to the generation model.
Option B is incorrect because semantic vector indexing differs from traditional lexical keyword indexing, although hybrid retrieval systems can use both.
Option D is incorrect as the primary definition because embeddings enable retrieval. Grounding retrieved evidence may reduce hallucinations, but embeddings do not independently guarantee factual accuracy.
The effectiveness of semantic retrieval depends on embedding quality, document segmentation, indexing, and relevance ranking.
Specific Cisco AITECH Course Guide References: Generative AI Ecosystem; Customizing AI Models; Exam Objective 1.5 - RAG, embeddings, and vector databases.
Cisco Learning Content


質問 # 86
Which algorithm category is used when no labels exist in data?

正解：B

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


質問 # 87
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