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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A company wants to build a chatbot that answers customer questions using a large language model. The company has an extensive internal knowledge base with accurate, up-to-date product information. To ensure the chatbot's answers are based on this reliable source rather than the model's internal knowledge, which technique should they use?
Why wrong: Fine-tuning can embed knowledge but requires retraining and is not dynamic; updates require retraining.
B
Zero-shot learning
Why wrong: Zero-shot learning uses no examples or external data and depends entirely on the model's pre-trained knowledge.
C
Grounding with retrieval-augmented generation
Grounding retrieves relevant documents or data from the knowledge base and provides them as context to the model, enabling accurate and current responses.
D
Prompt engineering with few-shot examples
Why wrong: Few-shot prompting uses a few examples to guide output style or format but does not incorporate external information for factual correctness.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Grounding with retrieval-augmented generation
Option C is correct because grounding with retrieval-augmented generation (RAG) retrieves relevant, up-to-date chunks from the internal knowledge base and provides them as context to the large language model (LLM) at inference time. This ensures the chatbot's answers are factually based on the company's reliable source rather than relying on the model's potentially outdated or incorrect parametric memory.
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.
✗
Fine-tuning the model on the knowledge base
Why it's wrong here
Fine-tuning can embed knowledge but requires retraining and is not dynamic; updates require retraining.
✗
Zero-shot learning
Why it's wrong here
Zero-shot learning uses no examples or external data and depends entirely on the model's pre-trained knowledge.
✓
Grounding with retrieval-augmented generation
Why this is correct
Grounding retrieves relevant documents or data from the knowledge base and provides them as context to the model, enabling accurate and current responses.
Related concept
Read the scenario before looking for a memorised answer.
✗
Prompt engineering with few-shot examples
Why it's wrong here
Few-shot prompting uses a few examples to guide output style or format but does not incorporate external information for factual correctness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning (which alters the model's internal knowledge) with retrieval-augmented generation (which keeps the model unchanged and instead supplies external context at query time), leading them to incorrectly select fine-tuning as the method to ensure answers come from a specific knowledge base.
Trap categories for this question
Command / output trap
Few-shot prompting uses a few examples to guide output style or format but does not incorporate external information for factual correctness.
Detailed technical explanation
How to think about this question
Under the hood, RAG uses a vector database (e.g., Azure Cognitive Search) to index the knowledge base, then at query time performs a similarity search to retrieve the top-k relevant document chunks. These chunks are inserted into the LLM's prompt as grounding context, effectively constraining the model to generate answers based solely on that retrieved information—a technique that mitigates hallucinations and ensures factual accuracy without modifying the model's weights.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Grounding with retrieval-augmented generation — Option C is correct because grounding with retrieval-augmented generation (RAG) retrieves relevant, up-to-date chunks from the internal knowledge base and provides them as context to the large language model (LLM) at inference time. This ensures the chatbot's answers are factually based on the company's reliable source rather than relying on the model's potentially outdated or incorrect parametric memory.
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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Question Discussion
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