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| Section | Weight | Objectives |
|---|
| Topic 1: Data Research and Analysis | | - Data analysis techniques
- 1. Data visualization
- 2. Statistical analysis
- Data collection and preparation
- 1. Data cleaning and preprocessing
- 2. Data gathering methodologies
|
| Topic 2: Agentic AI | 20% | - AI agent design principles
- 1. Orchestration
- 2. Human-in-the-loop (HITL) strategies
- Differentiation from Generative AI
- 1. Autonomous capabilities
- 2. Use cases comparison
- Data transformation and mapping - Model Context Protocol (MCP)
- 1. MCP framework primitives
- 2. Context in agentic AI
|
| Topic 3: AI for Code and Workflow Optimization | | - AI-assisted code development
- 1. Code review and debugging
- 2. Code generation
- Workflow automation
- 1. Process automation
- 2. Task optimization
|
| Topic 4: AI Ethics and Security | | - Ethical considerations in AI
- 1. Transparency and explainability
- 2. Bias and fairness
- Security threats and mitigations
- 1. Model security
- 2. Data privacy
|
| Topic 5: Prompt Engineering | 15% | - Security and safety
- 1. Defensive prompting methods
- 2. Improving response accuracy
- 3. Prompt injection attacks
- 4. Reducing hallucinations
- Prompt structures
- 1. Audio generation
- 2. Text generation
- 3. Image generation
- Principles and patterns
- 1. Roles
- 2. Instructions
- 3. Constraints
- Prompting techniques
- 1. Chained prompting
- 2. Few-shot prompting
- 3. Iterative prompting
|
| Topic 6: Generative AI Models | 20% | - Common use cases
- 1. Text summarization
- 2. Code generation
- 3. Content creation
- Model hosting strategies
- 1. Cloud-based vs. local deployments
- 2. Cost, latency, privacy, and scalability considerations
- 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
- Context windows and token management
- 1. Token limits
- 2. Response management
- Model selection
- 1. Reasoning and multimodal tasks
- 2. Selection from AI hubs
|
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Cisco AI Technical Practitioner Sample Questions (Q42-Q47):
NEW QUESTION # 42
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?
- A. to increase the speed of AI response
- B. to protect sensitive customer data
- C. to prevent unauthorized access to data
- D. to ensure the validity of AI decisions
Answer: D
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 # 43
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?
- A. orient
- B. correlate
- C. visualize
- D. hypothesize
Answer: D
Explanation:
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.
NEW QUESTION # 44
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?
- A. minimizing the input prompt size
- B. expanding the total context window
- C. increasing the model temperature
- D. increasing the model sampling rate
Answer: A
Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.
NEW QUESTION # 45
Which metric is commonly used to evaluate classification models?
- A. Accuracy
- B. Voltage
- C. Packet TTL
- D. Throughput only
Answer: A
Explanation:
Accuracy measures how many predictions were correct out of total predictions.
NEW QUESTION # 46
Why is defining a persona considered a best practice for technical prompt engineering?
- A. It guides the tone and vocabulary.
- B. It provides instructions for desired action.
- C. It reduces the total token usage.
- D. It provides a pattern to emulate.
Answer: A
Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.
NEW QUESTION # 47
......
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