Question 1
What is a typical example of generative AI in a business context?
Question 2
In Microsoft Copilot experiences, what is the primary benefit of grounding responses in enterprise data?
Question 3
Which approach helps reduce user errors when interacting with AI systems?
Question 4
Which learning approaches use labeled data? (Select all that apply)
Question 5
What does an LLM 'hallucination' typically mean?
Question 6
Why do AI service providers release multiple versions of the same model?
Question 7
If you need citations to enterprise sources, which request is best?
Question 8
Which factor most influences the quality of Copilot responses?
Question 9
In many LLMs, what does 'temperature' control?
Question 10
What is a key advantage of using Amazon Comprehend for end user applications?
Question 11
Which statement best reflects Microsoft’s guidance for users adopting AI tools?
Question 12
A user asks Copilot to generate content based on outdated information. What should the user do first?
Question 13
Which method is often used to fine-tune pretrained language models?
Question 14
Which request is most likely to keep the model within policy?
Question 15
When an AI system invents plausible but incorrect information, how should the user respond?
Question 16
Which AWS AI service would be most appropriate for automatically extracting text and data from scanned invoices and receipts?
Question 17
A strong 'role + task + context + constraints' prompt looks like:
Question 18
In AWS, which service is most directly aimed at helping non expert AI users build and deploy machine learning models with minimal infrastructure management?
Question 19
When is it reasonable to rely on AI output without additional human review?
Question 20
How can AI Copilot help in business communication?