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| Section | Weight | Objectives |
|---|
| AI Ethics and Security | | - Ethical considerations in AI
- 1. Bias and fairness
- 2. Transparency and explainability
- Security threats and mitigations
- 1. Model security
- 2. Data privacy
|
| Agentic AI | 20% | - Differentiation from Generative AI
- 1. Autonomous capabilities
- 2. Use cases comparison
- AI agent design principles
- 1. Orchestration
- 2. Human-in-the-loop (HITL) strategies
- Model Context Protocol (MCP)
- 1. Context in agentic AI
- 2. MCP framework primitives
- Data transformation and mapping
|
| Generative AI Models | 20% | - Context windows and token management
- 1. Response management
- 2. Token limits
- Model hosting strategies
- 1. Cost, latency, privacy, and scalability considerations
- 2. Cloud-based vs. local deployments
- Major generative model families
- 1. Large language models (LLMs)
- 2. Diffusion models
- Model selection
- 1. Reasoning and multimodal tasks
- 2. Selection from AI hubs
- Common use cases
- 1. Code generation
- 2. Content creation
- 3. Text summarization
- Retrieval Augmented Generation (RAG)
- 1. Grounding AI outputs in external knowledge
- 2. Vector databases
- 3. Embeddings
|
| Data Research and Analysis | | - Data collection and preparation
- 1. Data cleaning and preprocessing
- 2. Data gathering methodologies
- Data analysis techniques
- 1. Data visualization
- 2. Statistical analysis
|
| AI for Code and Workflow Optimization | | - Workflow automation
- 1. Task optimization
- 2. Process automation
- AI-assisted code development
- 1. Code review and debugging
- 2. Code generation
|
| Prompt Engineering | 15% | - Security and safety
- 1. Reducing hallucinations
- 2. Defensive prompting methods
- 3. Improving response accuracy
- 4. Prompt injection attacks
- Prompt structures
- 1. Text generation
- 2. Image generation
- 3. Audio generation
- Principles and patterns
- 1. Constraints
- 2. Instructions
- 3. Roles
- Prompting techniques
- 1. Iterative prompting
- 2. Few-shot prompting
- 3. Chained prompting
|
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Cisco AI Technical Practitioner Sample Questions (Q41-Q46):
NEW QUESTION # 41
How does a coordinator agent function in a multi-agent AI framework?
- A. It orchestrates task delegation across specialized agents and aggregates the outputs.
- B. It serves as the external data source that each agent queries for grounding information.
- C. It fine-tunes each agent's model weights to optimize performance on assigned tasks.
- D. It replaces the need for multiple agents by handling tasks within a single reasoning loop.
Answer: A
Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.
NEW QUESTION # 42
In supervised machine learning, what is required?
- A. Labeled datasets
- B. Random hardware failures
- C. No data
- D. Only encrypted traffic
Answer: A
NEW QUESTION # 43
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?
- A. multimodal model
- B. generative adversarial network
- C. embedding model
- D. diffusion model
Answer: A
Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.
NEW QUESTION # 44
What is hallucination in generative AI?
- A. AI generating incorrect or fabricated information
- B. Database replication
- C. GPU overheating
- D. Network congestion
Answer: A
Explanation:
Hallucination occurs when an AI confidently produces false or misleading outputs.
NEW QUESTION # 45
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. The number of tokens determines the per-request cost.
- B. Costs are determined by the number of API calls.
- C. Tokenization increases cost by requiring additional GPU run time.
- D. Tokenization has no impact because pricing is based on a flat-rate subscription.
Answer: A
Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.
NEW QUESTION # 46
......
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