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| Section | Weight | Objectives |
|---|
| Prompt Engineering | 15% | - Prompt structures
- 1. Text generation
- 2. Image generation
- 3. Audio generation
- Prompting techniques
- 1. Few-shot prompting
- 2. Chained prompting
- 3. Iterative prompting
- Security and safety
- 1. Defensive prompting methods
- 2. Improving response accuracy
- 3. Prompt injection attacks
- 4. Reducing hallucinations
- Principles and patterns
- 1. Roles
- 2. Instructions
- 3. Constraints
|
| Data Research and Analysis | | - Data collection and preparation
- 1. Data gathering methodologies
- 2. Data cleaning and preprocessing
- Data analysis techniques
- 1. Statistical analysis
- 2. Data visualization
|
| Agentic AI | 20% | - Differentiation from Generative AI
- 1. Autonomous capabilities
- 2. Use cases comparison
- AI agent design principles
- 1. Human-in-the-loop (HITL) strategies
- 2. Orchestration
- Data transformation and mapping - Model Context Protocol (MCP)
- 1. MCP framework primitives
- 2. Context in agentic AI
|
| AI Ethics and Security | | - Security threats and mitigations
- 1. Data privacy
- 2. Model security
- Ethical considerations in AI
- 1. Bias and fairness
- 2. Transparency and explainability
|
| 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
|
| Generative AI Models | 20% | - Model hosting strategies
- 1. Cloud-based vs. local deployments
- 2. Cost, latency, privacy, and scalability considerations
- Common use cases
- 1. Content creation
- 2. Code generation
- 3. Text summarization
- Retrieval Augmented Generation (RAG)
- 1. Grounding AI outputs in external knowledge
- 2. Vector databases
- 3. Embeddings
- Model selection
- 1. Selection from AI hubs
- 2. Reasoning and multimodal tasks
- Context windows and token management
- 1. Response management
- 2. Token limits
- Major generative model families
- 1. Large language models (LLMs)
- 2. Diffusion models
|
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Cisco AI Technical Practitioner Sample Questions (Q48-Q53):
NEW QUESTION # 48
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 # 49
Which metric is commonly used to evaluate classification models?
- A. Voltage
- B. Packet TTL
- C. Accuracy
- D. Throughput only
Answer: C
Explanation:
Accuracy measures how many predictions were correct out of total predictions.
NEW QUESTION # 50
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. injection mitigation
- B. approval gate
- C. data lineage
- D. drift monitoring
Answer: B
Explanation:
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.
NEW QUESTION # 51
How does AI support exploratory data analysis when working with large, unstructured datasets?
- A. by performing bit-by-bit reconstruction
- B. by identifying patterns and groupings
- C. by compressing the datasets
- D. by tuning inference parameters
Answer: B
Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.
NEW QUESTION # 52
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. diffusion model
- B. multimodal model
- C. generative adversarial network
- D. embedding model
Answer: B
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 # 53
......
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