PDF Version DemoCisco 810-110 Exam Overview:
| Certification Vendor: | Cisco |
| Exam Name: | Cisco AI Technical Practitioner v1.0 (AITECH) |
| Exam Number: | 810-110 |
| Real Exam Qty: | 40 |
| Exam Price: | USD 150 |
| Certificate Validity Period: | 3 years |
| Available Languages: | English |
| Passing Score: | Pass/Fail |
| Exam Format: | Multiple Choice |
| Exam Duration: | 60 minutes |
| Recommended Training: | Cisco AI Technical Practitioner Training |
| Exam Registration: | Cisco Official Exam Page |
| Sample Questions: | Cisco 810-110 Sample Questions |
| Exam Way: | Online proctored or at authorized test centers |
| Pre Condition: | No formal prerequisites |
| Official Syllabus URL: | https://learningcontent.cisco.com/documents/marketing/exam-topics/810-110-AITECH-v1.0.pdf |
Cisco 810-110 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic AI | 15% | - Human-in-the-loop strategies - Agent design principles and capabilities - Model Context Protocol (MCP) framework - Agentic AI vs Generative AI use cases |
| Topic 2: Prompt Engineering | 15% | - Defensive prompting and error mitigation - Advanced prompting techniques - Prompt design principles and best practices |
| Topic 3: Data Research and Analysis | 15% | - Data quality, bias and validation - Data collection, preparation and preprocessing - Data visualization and insight generation |
| Topic 4: AI for Code and Workflow Optimization | 15% | - AI-assisted code generation and improvement - Automation and workflow enhancement - Integration of AI tools in development processes |
| Topic 5: AI Ethics and Security | 20% | - Data privacy and security in AI systems - Compliance and governance frameworks - AI-specific threats, risks and misinformation - Responsible AI principles |
| Topic 6: Generative AI Models | 20% | - Model hosting options and trade-offs - Model selection and evaluation in AI hubs - Context windows, token limits and response management - Generative AI model families and use cases |
Cisco AI Technical Practitioner Sample Questions:
1. Which Cisco AI capability helps detect abnormal network behavior?
A) BIOS recovery mode
B) Static routing
C) Manual packet counting
D) AI-driven anomaly detection
2. A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?
A) variational autoencoders
B) large language models
C) diffusion models
D) generative adversarial networks
3. Which approach protects PII when utilizing a public AI service for data analysis?
A) utilizing system prompts to instruct the AI to ignore sensitive data fields
B) anonymizing or redacting PH from the dataset before transmitting the request
C) encrypting the data payload during transit to the AI service provider
D) ensuring the AI provider maintains industry-standard SOC 2 compliance
4. 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?
A) Tokenization has no impact because pricing is based on a flat-rate subscription.
B) The number of tokens determines the per-request cost.
C) Costs are determined by the number of API calls.
D) Tokenization increases cost by requiring additional GPU run time.
5. 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) approval gate
B) drift monitoring
C) injection mitigation
D) data lineage
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: A |
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