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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Generative AI Models | 20% | - Context windows, token limits and response management - Generative AI model families and use cases - Model selection and evaluation in AI hubs - Model hosting options and trade-offs |
| Topic 2: Data Research and Analysis | 15% | - Data visualization and insight generation - Data quality, bias and validation - Data collection, preparation and preprocessing |
| Topic 3: Prompt Engineering | 15% | - Advanced prompting techniques - Defensive prompting and error mitigation - Prompt design principles and best practices |
| Topic 4: Agentic AI | 15% | - Agentic AI vs Generative AI use cases - Agent design principles and capabilities - Human-in-the-loop strategies - Model Context Protocol (MCP) framework |
| Topic 5: AI Ethics and Security | 20% | - AI-specific threats, risks and misinformation - Responsible AI principles - Data privacy and security in AI systems - Compliance and governance frameworks |
| Topic 6: AI for Code and Workflow Optimization | 15% | - Automation and workflow enhancement - AI-assisted code generation and improvement - Integration of AI tools in development processes |
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NEW QUESTION # 41
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?
Answer: A
Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.
NEW QUESTION # 42
A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?
Answer: B
Explanation:
Prompt chaining breaks a complex task into a sequence of connected prompts, where each response becomes part of the input for the next step.
NEW QUESTION # 43
How does AI support exploratory data analysis when working with large, unstructured datasets?
Answer: A
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 # 44
Which AI model type is best suited for image recognition tasks?
Answer: D
Explanation:
CNNs are optimized for image and visual pattern recognition.
NEW QUESTION # 45
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?
Answer: B
Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.
NEW QUESTION # 46
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