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Question 1 of 10 · Fundamentals of AI and ML
How does machine learning differ from traditional programming?
ML derives rules from data; traditional programming codes rules directly.
Question 2 of 10 · Fundamentals of Generative AI
What is an embedding in generative AI?
Embeddings represent meaning as vectors, enabling search and similarity.
Question 3 of 10 · Applications of Foundation Models
What should you consider about latency when choosing a model?
Latency must fit the application's responsiveness needs.
Question 4 of 10 · Guidelines for Responsible AI
Why involve humans in high-stakes AI decisions?
Human oversight adds a check on consequential decisions.
Question 5 of 10 · Security, Compliance, and Governance for AI Solutions
Which principle limits what an AI service account can access?
Least privilege limits the AI service's access to what it needs.
Question 6 of 10 · Fundamentals of AI and ML
What is machine learning?
Machine learning learns patterns from data rather than following explicitly coded rules.
Question 7 of 10 · Fundamentals of Generative AI
What does zero-shot prompting mean?
Zero-shot gives no examples; few-shot gives a few.
Question 8 of 10 · Applications of Foundation Models
Which factor should guide choosing a foundation model for a use case?
Model choice balances capability, cost, latency and licensing against the need.
Question 9 of 10 · Guidelines for Responsible AI
What does explainability provide for an AI decision?
Explainability describes what drove a decision.
Question 10 of 10 · Security, Compliance, and Governance for AI Solutions
What is a key privacy concern when using generative AI?
Prompts may contain personal data that needs governance over storage and training use.
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Fundamentals of AI and ML
Fundamentals of Generative AI
Applications of Foundation Models
Guidelines for Responsible AI
Security, Compliance, and Governance for AI Solutions