- A
Linking multiple AI models in a pipeline where each model's output feeds the next
Why wrong: Model chaining is an AI architecture pattern — chain-of-thought is a single-model prompting technique for eliciting explicit reasoning.
- B
Prompting the model to show explicit reasoning steps before giving a final answer
CoT prompting ('think step by step') improves multi-step reasoning by externalising the reasoning process — most effective for maths and logic.
- C
Training a model on a sequence of related documents to build contextual knowledge
Why wrong: Sequential document training is fine-tuning — CoT is an inference-time prompting technique requiring no model changes.
- D
A method for connecting chatbot conversation turns to maintain long-term memory
Why wrong: Long-term memory is a conversational AI architecture concern — CoT is a prompting strategy for improving step-by-step reasoning.
Quick Answer
The correct answer is prompting the model to show explicit reasoning steps before giving a final answer. This technique, known as chain-of-thought prompting, works by instructing the AI to break down a complex problem into intermediate, logical steps rather than jumping straight to a conclusion, which mirrors how a human might solve a multi-step math problem or a logic puzzle. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to improve model accuracy for tasks requiring multi-step reasoning, such as arithmetic or commonsense inference, and it often appears as a scenario where you need to choose the method that reduces shortcut errors. A common trap is confusing it with simple zero-shot prompting, which lacks the step-by-step structure. Memory tip: think of it as “show your work” for AI—just like a math teacher wants to see each step, chain-of-thought prompting forces the model to reveal its reasoning before the final answer.
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.
What is 'chain-of-thought prompting' and when is it most effective?
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
Prompting the model to show explicit reasoning steps before giving a final answer
Chain-of-thought prompting instructs the model to break down a complex problem into intermediate reasoning steps before producing the final answer. This technique improves accuracy on tasks requiring multi-step logic, such as arithmetic, commonsense reasoning, or symbolic manipulation, by making the model's internal reasoning explicit and reducing errors from shortcut answers.
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.
- ✗
Linking multiple AI models in a pipeline where each model's output feeds the next
Why it's wrong here
Model chaining is an AI architecture pattern — chain-of-thought is a single-model prompting technique for eliciting explicit reasoning.
- ✓
Prompting the model to show explicit reasoning steps before giving a final answer
Why this is correct
CoT prompting ('think step by step') improves multi-step reasoning by externalising the reasoning process — most effective for maths and logic.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Training a model on a sequence of related documents to build contextual knowledge
Why it's wrong here
Sequential document training is fine-tuning — CoT is an inference-time prompting technique requiring no model changes.
- ✗
A method for connecting chatbot conversation turns to maintain long-term memory
Why it's wrong here
Long-term memory is a conversational AI architecture concern — CoT is a prompting strategy for improving step-by-step reasoning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'chain-of-thought prompting' with 'model chaining' or 'pipeline architectures' (Option A), because both involve a sequence, but chain-of-thought is a single-model prompting technique, not a multi-model workflow.
Detailed technical explanation
How to think about this question
Chain-of-thought prompting works by providing few-shot examples that include intermediate reasoning steps (e.g., 'Step 1: ... Step 2: ... Therefore, the answer is ...'), which conditions the model to generate similar step-by-step reasoning for new queries. Under the hood, this leverages the autoregressive nature of transformer models, where each token prediction benefits from the preceding reasoning context, effectively reducing the likelihood of arithmetic or logical errors. A real-world scenario is solving multi-step math word problems, where a direct answer often fails but chain-of-thought yields correct results by decomposing the problem.
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: Prompting the model to show explicit reasoning steps before giving a final answer — Chain-of-thought prompting instructs the model to break down a complex problem into intermediate reasoning steps before producing the final answer. This technique improves accuracy on tasks requiring multi-step logic, such as arithmetic, commonsense reasoning, or symbolic manipulation, by making the model's internal reasoning explicit and reducing errors from shortcut answers.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'chain of thought' prompting in generative AI?
medium- A.Connecting multiple AI models in a processing pipeline
- ✓ B.A prompting technique that elicits step-by-step reasoning to improve accuracy on complex tasks
- C.Linking multiple conversation turns to maintain context
- D.Training a model using sequential text data only
Why B: Chain of thought prompting is a technique where the model is asked to produce intermediate reasoning steps before arriving at a final answer, which significantly improves performance on multi-step arithmetic, logic, and common-sense reasoning tasks. Unlike a simple answer request, it forces the model to externalize its reasoning process, reducing errors from shortcut or pattern-matching behaviors. This is a prompting strategy, not a model architecture change, and is particularly effective in large language models like GPT-4 or Azure OpenAI's GPT-3.5 Turbo.
Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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