AI-900 Practice Question: Describe features of generative AI workloads on Azure

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Completions API

  Why it's wrong here

  The older Completions API is designed for single-turn, free-form text continuation from a standalone prompt. It does not manage a conversation history or track roles, so to simulate multi-turn dialogue you would have to manually concatenate every past user and assistant message into one giant string. That approach quickly grows unwieldy, loses clear context boundaries, and is why Chat Completions—with its structured message list—is the appropriate API for an assistant.

  When this WOULD be correct

  A question asks: 'Which Azure OpenAI API endpoint should be used to generate a one-off response to a user query without any conversation history?' In that case, the Completions API is correct because it handles single-turn prompts without needing to manage context.

• ✓

  Chat Completions API

  Why this is correct

  The Chat Completions API is Azure OpenAI's purpose-built endpoint for conversational AI. It accepts an array of message objects with explicit roles (system, user, and assistant), allowing the model to maintain context and refer to earlier parts of the dialogue across multiple turns. Each call returns a model-generated assistant reply that continues the conversation seamlessly, which is exactly what a multi-turn AI assistant requires.

• ✗

  Embeddings API

  Why it's wrong here

  The Embeddings API is fundamentally different: it transforms input text into a high-dimensional numerical vector that encodes semantic meaning. These vectors are used to measure similarity between texts, powering search, recommendations, or clustering, but they do not generate language or produce conversational replies. Calling this API would not return a coherent assistant response, so it is unsuitable for powering a chat assistant.

  When this WOULD be correct

  A question asks: 'Which API should be used to convert product descriptions into vector representations for a semantic search system that finds similar troubleshooting articles?' The Embeddings API is correct for generating vector embeddings for similarity search.

• ✗

  Fine-tuning

  Why it's wrong here

  Fine-tuning is a training-time process, not an inference-time API. It adjusts a pre-trained model's weights using a custom dataset to specialize its behavior for specific tasks or domain language. After fine-tuning, you still need a dedicated inference endpoint such as the Chat Completions API to actually run conversations; fine-tuning itself cannot accept a user message and produce a reply, so it is the wrong choice for this production assistant.

  When this WOULD be correct

  A company has a specialized dataset of product troubleshooting logs and wants to create a custom model that better understands their specific terminology and solutions. In that scenario, Fine-tuning would be the correct answer.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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