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| Section | Weight | Objectives |
|---|
| Topic 1: Agentic AI | 20% | - AI agent design principles
- 1. Orchestration
- 2. Human-in-the-loop (HITL) strategies
- Differentiation from Generative AI
- 1. Use cases comparison
- 2. Autonomous capabilities
- Model Context Protocol (MCP)
- 1. MCP framework primitives
- 2. Context in agentic AI
- Data transformation and mapping
|
| Topic 2: Generative AI Models | 20% | - Major generative model families
- 1. Large language models (LLMs)
- 2. Diffusion models
- Retrieval Augmented Generation (RAG)
- 1. Grounding AI outputs in external knowledge
- 2. Vector databases
- 3. Embeddings
- 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
- Context windows and token management
- 1. Token limits
- 2. Response management
- Model selection
- 1. Reasoning and multimodal tasks
- 2. Selection from AI hubs
|
| Topic 3: Data Research and Analysis | | - Data collection and preparation
- 1. Data gathering methodologies
- 2. Data cleaning and preprocessing
- Data analysis techniques
- 1. Data visualization
- 2. Statistical analysis
|
| Topic 4: Prompt Engineering | 15% | - Principles and patterns
- 1. Constraints
- 2. Roles
- 3. Instructions
- Prompt structures
- 1. Image generation
- 2. Audio generation
- 3. Text generation
- Prompting techniques
- 1. Few-shot prompting
- 2. Chained prompting
- 3. Iterative prompting
- Security and safety
- 1. Improving response accuracy
- 2. Prompt injection attacks
- 3. Reducing hallucinations
- 4. Defensive prompting methods
|
| Topic 5: AI Ethics and Security | | - Ethical considerations in AI
- 1. Bias and fairness
- 2. Transparency and explainability
- Security threats and mitigations
- 1. Data privacy
- 2. Model security
|
| Topic 6: 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
|
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Cisco AI Technical Practitioner Sample Questions (Q31-Q36):
NEW QUESTION # 31
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?
- A. to ensure the validity of AI decisions
- B. to prevent unauthorized access to data
- C. to protect sensitive customer data
- D. to increase the speed of AI response
Answer: A
Explanation:
Human-in-the-Loop governance ensures that a qualified person reviews, validates, and can override AI-generated decisions when outcomes may significantly affect people, such as medical diagnoses, treatment decisions, credit approvals, or financial risk assessments.
NEW QUESTION # 32
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?
- A. knowledge cut-off
- B. data poisoning
- C. misinformation
- D. disinformation
Answer: A
Explanation:
Knowledge cut-off occurs when a model lacks information about events, standards, or specifications released after its training data period, causing it to provide outdated information.
NEW QUESTION # 33
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?
- A. response creativity level of the model
- B. release frequency of the model
- C. parameter count of the model
- D. training duration of the model
Answer: C
Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.
NEW QUESTION # 34
What is the role of the client in the Model Context Protocol?
- A. to act as a tool or service provider
- B. to act as an LLM for inferencing
- C. to provide resources for task execution
- D. to orchestrate interactions with servers
Answer: D
Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.
NEW QUESTION # 35
What is occurring when an agent converts a CSV list into a Markdown table for a final report?
- A. data transformation
- B. data filtering
- C. data quantization
- D. data classification
Answer: A
Explanation:
Data transformation changes data from one format or structure into another, such as converting CSV rows into a Markdown table for reporting.
NEW QUESTION # 36
......
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