- A
Training an AI model from scratch on domain-specific data
Why wrong: Training from scratch is expensive model development — grounding connects an existing model to specific knowledge at inference time.
- B
Connecting AI responses to verified information from a knowledge base to improve accuracy
Grounding (via RAG) retrieves relevant facts from a knowledge base and provides them as context, anchoring the model's responses to verified information.
- C
Removing biases from a trained language model
Why wrong: Bias reduction is a fairness/alignment technique — grounding is about factual accuracy through knowledge retrieval.
- D
Converting text responses into speech output
Why wrong: Text-to-speech is an audio synthesis capability — grounding is about factual accuracy of text responses.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is grounding in the context of generative AI and Retrieval Augmented Generation (RAG)?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Connecting AI responses to verified information from a knowledge base to improve accuracy
Grounding in generative AI and RAG refers to the process of constraining a language model's output to information retrieved from a trusted, external knowledge base (e.g., Azure Cognitive Search, vector databases). This prevents the model from generating hallucinated or outdated content by anchoring its responses to verified facts, which is essential for enterprise applications requiring high accuracy.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Training an AI model from scratch on domain-specific data
Why it's wrong here
Training from scratch is expensive model development — grounding connects an existing model to specific knowledge at inference time.
- ✓
Connecting AI responses to verified information from a knowledge base to improve accuracy
Why this is correct
Grounding (via RAG) retrieves relevant facts from a knowledge base and provides them as context, anchoring the model's responses to verified information.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Removing biases from a trained language model
Why it's wrong here
Bias reduction is a fairness/alignment technique — grounding is about factual accuracy through knowledge retrieval.
- ✗
Converting text responses into speech output
Why it's wrong here
Text-to-speech is an audio synthesis capability — grounding is about factual accuracy of text responses.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse grounding with fine-tuning (Option A), because both improve model output quality, but grounding is a prompt-time technique that does not alter model weights, whereas fine-tuning updates the model itself.
Detailed technical explanation
How to think about this question
Under the hood, grounding in RAG works by first embedding the user query into a vector representation, then performing a similarity search (e.g., cosine similarity) against a vector index of documents. The top-k retrieved chunks are inserted into the prompt as context before the model generates a response, effectively 'grounding' the output in those retrieved facts. A real-world scenario is a customer support chatbot that uses Azure AI Search to pull the latest product documentation, ensuring answers reflect current policies rather than the model's training cutoff.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Connecting AI responses to verified information from a knowledge base to improve accuracy — Grounding in generative AI and RAG refers to the process of constraining a language model's output to information retrieved from a trusted, external knowledge base (e.g., Azure Cognitive Search, vector databases). This prevents the model from generating hallucinated or outdated content by anchoring its responses to verified facts, which is essential for enterprise applications requiring high accuracy.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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