- A
Connecting multiple AI models in a processing pipeline
Why wrong: Connecting models is an orchestration pattern — chain of thought is a specific prompting technique for step-by-step reasoning.
- B
A prompting technique that elicits step-by-step reasoning to improve accuracy on complex tasks
CoT prompting makes the model reason through steps explicitly — dramatically improving performance on multi-step reasoning tasks.
- C
Linking multiple conversation turns to maintain context
Why wrong: Conversation context is managed by conversation history — CoT is a prompting technique for single-response reasoning.
- D
Training a model using sequential text data only
Why wrong: Sequential training data is about dataset format — CoT is a prompting technique used at inference time.
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 'chain of thought' prompting in generative AI?
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
A prompting technique that elicits step-by-step reasoning to improve accuracy on complex tasks
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.
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.
- ✗
Connecting multiple AI models in a processing pipeline
Why it's wrong here
Connecting models is an orchestration pattern — chain of thought is a specific prompting technique for step-by-step reasoning.
- ✓
A prompting technique that elicits step-by-step reasoning to improve accuracy on complex tasks
Why this is correct
CoT prompting makes the model reason through steps explicitly — dramatically improving performance on multi-step reasoning tasks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Linking multiple conversation turns to maintain context
Why it's wrong here
Conversation context is managed by conversation history — CoT is a prompting technique for single-response reasoning.
- ✗
Training a model using sequential text data only
Why it's wrong here
Sequential training data is about dataset format — CoT is a prompting technique used at inference time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'chain of thought' with 'chaining models' (Option A) because both involve the word 'chain', but chain of thought is a single-model prompting technique, not a multi-model pipeline.
Detailed technical explanation
How to think about this question
Under the hood, chain of thought works by leveraging the model's autoregressive nature: each intermediate step conditions the next token prediction, effectively creating a scratchpad that distributes the computational load across multiple decoding steps. This technique is especially valuable for tasks like math word problems (e.g., GSM8K) where direct answer generation often fails due to arithmetic errors, but step-by-step reasoning yields near-perfect accuracy. In Azure OpenAI, you can implement this by appending 'Let's think step by step' to your prompt, which triggers the model to generate reasoning chains before the final answer.
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: A prompting technique that elicits step-by-step reasoning to improve accuracy on complex tasks — 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.
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
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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