Question 560 of 1,020

Describe features of generative AI workloads on Azure →mediumMultiple ChoiceObjective-mapped

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 the primary benefit of using Retrieval Augmented Generation (RAG) over relying solely on an LLM's trained knowledge?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "primary"
Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

A
RAG makes LLMs faster by skipping the training process
Why wrong: RAG doesn't affect training — it augments inference by retrieving relevant documents at query time.
B
RAG grounds LLM responses in current, specific information — reducing hallucination and knowledge cutoff issues
RAG retrieves relevant facts from a knowledge base at query time, making LLM responses more accurate and up-to-date than relying on training data alone.
C
RAG reduces the cost of API calls by batching requests
Why wrong: RAG doesn't reduce API costs — it improves response accuracy through retrieval-augmented context.
D
RAG allows LLMs to process images alongside text
Why wrong: Multimodal processing requires multimodal models — RAG is about retrieving text documents to provide as context.

Full breakdown with real-world context →

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

✓

RAG grounds LLM responses in current, specific information — reducing hallucination and knowledge cutoff issues

RAG enhances LLM outputs by retrieving relevant, up-to-date information from an external knowledge base (e.g., Azure Cognitive Search) and injecting it into the prompt context. This grounds the model's response in verifiable data, significantly reducing hallucinations and overcoming the knowledge cutoff limitation inherent in static training data.

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.

✗
RAG makes LLMs faster by skipping the training process
Why it's wrong here
RAG doesn't affect training — it augments inference by retrieving relevant documents at query time.
✓
RAG grounds LLM responses in current, specific information — reducing hallucination and knowledge cutoff issues
Why this is correct
RAG retrieves relevant facts from a knowledge base at query time, making LLM responses more accurate and up-to-date than relying on training data alone.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
RAG reduces the cost of API calls by batching requests
Why it's wrong here
RAG doesn't reduce API costs — it improves response accuracy through retrieval-augmented context.
✗
RAG allows LLMs to process images alongside text
Why it's wrong here
Multimodal processing requires multimodal models — RAG is about retrieving text documents to provide as context.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse RAG with general LLM optimization techniques (like fine-tuning or prompt engineering) and assume it improves speed or reduces cost, when in fact its primary value is factual grounding and recency.

Detailed technical explanation

How to think about this question

Under the hood, RAG uses a retriever (e.g., dense passage retrieval with embeddings) to fetch the top-k relevant document chunks from a vector index, then concatenates them with the user query to form a grounded prompt for the LLM. This approach is critical in enterprise scenarios like customer support, where the LLM must answer based on the latest product documentation rather than its training data, which may be months or years old.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: RAG grounds LLM responses in current, specific information — reducing hallucination and knowledge cutoff issues — RAG enhances LLM outputs by retrieving relevant, up-to-date information from an external knowledge base (e.g., Azure Cognitive Search) and injecting it into the prompt context. This grounds the model's response in verifiable data, significantly reducing hallucinations and overcoming the knowledge cutoff limitation inherent in static training data.

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.

Are there clue words in this question I should notice?

Yes — watch for: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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