CISCOCisco AI Technical Practitioner
The Cisco AI Technical Practitioner certification validates your ability to modernize code, automate workflows, and design AI-powered solutions through hands-on labs and real-world scenarios. Built for IT professionals who want to embed generative AI into daily operations, this credential proves you can apply prompt engineering, AI ethics, and agentic AI to drive adoption and innovation within your organization.
607 practice questions · Updated 2026-07-30
6Domains
27Objectives
142Concepts
607Questions
AI-TECHNICAL-PRACTITIONER Curriculum
Every domain, objective, and concept the AI-TECHNICAL-PRACTITIONER exam measures.
- Generative AI model families overview
- Large Language Models (LLMs)
- Diffusion models
- Common use cases for LLMs
- Common use cases for diffusion models
- Matching model families to use cases
- Cloud-hosted vs locally hosted model hosting
- Cost trade-offs of hosting options
- Latency trade-offs of hosting options
- Privacy trade-offs of hosting options
- Scalability trade-offs of hosting options
- Context windows
- Token limits
- Response management
- AI model hubs and repositories
- Model selection criteria
- Use-case-driven model selection
- Reasoning models
- Multimodal models
- Evaluating model suitability
- RAG architecture
- Embeddings in RAG
- Vector databases
- RAG vs fine-tuning
- Role-based prompting
- Instruction-based prompting
- Constraint-based prompting
- Patterns in prompt engineering
- Iterative/Sequential Prompting
- Chained Prompting
- Few-Shot Prompting
- Prompt Structures for Text Generation
- Prompt Structures for Image Generation
- Prompt Structures for Audio Generation
- Definition of prompt injection
- Direct prompt injection
- Indirect prompt injection
- Goal hijacking
- Prompt leaking
- Jailbreaking
- Payload splitting
- Context manipulation
- Role-playing and persona injection
- Encoding and obfuscation techniques
- Defensive Prompting Overview
- Hallucination Types and Causes
- Prompt Structuring Techniques
- Grounding and Context Provision
- Constraint and Format Specification
- Verification and Cross-Checking Prompts
- Uncertainty Expression and Confidence Elicitation
- Iterative Refinement and Feedback Loops
- Mitigation Strategies for Hallucinations
- Define responsible AI
- Explain fairness in AI
- Explain transparency in AI
- Explain accountability in AI
- Explain bias mitigation
- Explain AI safety
- Data Privacy Principles in AI
- Data Security Measures for AI
- Privacy-Preserving AI Techniques
- Compliance and Governance in AI
- AI-specific security threats
- Risks in AI deployment
- Misinformation in AI
- Mitigation strategies
- AI governance frameworks
- AI policy development
- AI risk management
- AI compliance and regulations
- AI governance roles and responsibilities
- AI governance in practice
- Role of AI in EDA
- AI-driven data profiling
- Automated visualization
- Pattern and correlation discovery
- Handling missing and noisy data
- Feature importance and selection
- Iterative hypothesis testing
- Limitations and considerations
- Quality Checks
- Formatting
- Transformation
- Cleaning
- Ethical principles in AI-assisted data analysis
- Privacy considerations in AI data analysis
- Data exposure controls
- Regulatory and compliance frameworks
- AI-assisted research techniques
- AI-assisted ideation techniques
- AI-assisted content drafting techniques
- AI in Requirements Gathering
- AI in Prototyping
- AI in Implementation
- AI in Testing
- AI in Deployment
- AI-Driven SDLC Integration
- AI code generation capabilities
- Rapid prototyping with AI
- Use cases for AI code generation
- Limitations and considerations
- AI workflow design principles
- AI workflow monitoring principles
- Workflow orchestration
- Feedback loops in AI workflows
- Ethical and governance considerations
- Token usage fundamentals
- Context-window limits
- Impact on prototyping cost
- Impact on latency
- Impact on output quality
- Token management strategies
- AI-assisted debugging
- AI for error handling
- AI-generated documentation
- Definition of Agentic AI
- Definition of Generative AI
- Use Cases of Agentic AI
- Use Cases of Generative AI
- Differentiating Factors
- Overlap and Combination
- Agent Design Principles
- Autonomous Capabilities
- Orchestration Concepts
- MCP architecture
- MCP primitives
- Tools in MCP
- Resources in MCP
- Prompts in MCP
- MCP in agentic workflows
- Define HITL
- Identify HITL roles
- Explain HITL benefits
- Describe HITL challenges
- Apply HITL in agentic AI
- Data transformation in AI agents
- Data mapping in AI agents
- Transformation techniques
- Mapping strategies
- Handling data inconsistencies
- Integration with agent workflows
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for AI-TECHNICAL-PRACTITIONER, so none is invented.