Cisco 810-110 Testking, 810-110 Unterlage

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Cisco 810-110 Exam Syllabus Topics:

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

>> Cisco 810-110 Testking <<

Echte 810-110 Fragen und Antworten der 810-110 Zertifizierungsprüfung

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Cisco AI Technical Practitioner 810-110 Prüfungsfragen mit Lösungen (Q95-Q100):

95. Frage
Which AI capability supports the Design and Prototyping phase of the SDLC when establishing initial API contracts and endpoint structures for a new microservice?

Antwort: A

Begründung:
The Design and Prototyping phase of the Software Development Lifecycle translates approved requirements into preliminary technical architectures, interface definitions, and implementation specifications. Option B is correct because AI can analyze functional requirements and recommend initial system blueprints, API contracts, and endpoint structures before full development begins.
For example, when designing a microservice for inventory management, an AI assistant can propose REST endpoints such as GET /products, POST /products, and PATCH /products/{id}.
It can also recommend request and response schemas, HTTP status codes, authentication requirements, and suitable service boundaries.
AI-generated OpenAPI specifications can provide an initial contract for collaboration between frontend developers, backend engineers, and integration teams.
Option A is incorrect because deploying a service to Kubernetes belongs primarily to the deployment phase.
Option C is incorrect because comprehensive validation of finalized production code is associated with testing activities.
Option D is incorrect because extracting user stories and acceptance criteria from meeting transcripts primarily supports requirements engineering.
AI-assisted prototyping accelerates architectural exploration, but generated designs require validation against security, scalability, maintainability, and integration requirements.
Specific Cisco AITECH Course Guide References:
* AI-Driven Software Engineering
* AI Systems Integration with APIs
* Exam Objective 5.1: AI across SDLC phases
* Exam Objective 5.2: AI-assisted code generation and rapid prototyping


96. Frage
A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?

Antwort: A

Begründung:
Transparency means clearly informing users when AI is being used, what it is doing, and how it may affect their data or experience.


97. Frage
In supervised machine learning, what is required?

Antwort: D

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


98. Frage
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

Antwort: A

Begründung:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.


99. Frage
An attacker deliberately adds corrupted records to an AI training dataset, marking insecure code as "best practices" so that the model recommends vulnerable code in production. Which security threat does this describe?

Antwort: B

Begründung:
Data poisoning is an adversarial machine learning technique in which attackers deliberately manipulate information used during model training or adaptation to influence subsequent model behavior. Option C is correct because the attacker contaminates training records with misleading examples that incorrectly label insecure programming practices as acceptable.
During model development, training examples contribute to the patterns learned by the model.
If malicious records are incorporated into the training dataset, the model may internalize incorrect associations and reproduce them when generating recommendations.
For example, corrupted training examples might repeatedly describe unsafe authentication practices or inadequate input validation as recommended implementations.
A coding assistant influenced by these examples could generate insecure code despite appearing to follow legitimate programming instructions.
Option A is incorrect because prompt injection typically targets model behavior during inference through adversarial instructions.
Option B is incorrect because model inversion attempts to recover sensitive training information or characteristics through model interactions.
Option D is incorrect because overfitting occurs when a model learns training-specific patterns that fail to generalize effectively. It does not necessarily involve malicious manipulation.
Defensive measures include training-data provenance verification, integrity controls, dataset auditing, anomaly detection, and security-focused model evaluation.
Specific Cisco AITECH Course Guide References:
* Generative AI Security and Privacy Fundamentals
* Debugging and Correcting AI-Generated Outputs
* Exam Objective 3.3: AI-specific security threats and risks
* Exam Objective 3.4: AI governance and risk management


100. Frage
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