AI-900 Practice Question: Describe features of generative AI workloads on Azure
What is 'chain of thought' prompting in generative AI?
⚠ Common exam trap
Candidates often 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.
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.
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 multiple AI models in a processing pipeline is an orchestration pattern, where outputs from one service become inputs to another—for example, OCR followed by translation. Chain-of-thought is not architectural; it is a single-model prompting strategy where reasoning is elicited inside one response. No external components are involved.
- ✓
A prompting technique that elicits step-by-step reasoning to improve accuracy on complex tasks
Why this is correct
A prompting technique that elicits step-by-step reasoning improves accuracy on complex tasks by instructing the model to decompose problems, such as including 'think step by step' in the prompt. The model generates intermediate reasoning tokens before the final answer, which reduces arithmetic and logical errors. This is the correct definition of chain-of-thought.
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Linking multiple conversation turns to maintain context
Why it's wrong here
Linking multiple conversation turns to maintain context is handled by conversation history, which keeps prior user and assistant messages in the context window. Chain-of-thought operates within a single response and does not update or preserve memory across turns. It concerns internal reasoning, not dialogue state.
- ✗
Training a model using sequential text data only
Why it's wrong here
Training a model using sequential text data only describes the dataset format or pretraining objective, such as next-token prediction on token sequences. Chain-of-thought is not a training method; it is an inference-time prompting technique that works in zero-shot or few-shot settings without changing weights or data.
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Related to this question
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Azure Machine Learning Studio
Key term
Generative AI
Generative AI is a type of artificial intelligence that creates new content—like text, images, or code—by learning patterns from existing data.
Key term
Azure OpenAI Service
Azure OpenAI Service is a cloud platform from Microsoft that lets developers use powerful artificial intelligence models, like GPT-4, to build applications that can understand and generate human-like text, code, images, and more.
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