The correct combination is GPT-4 for text and DALL-E for images, as this pairing directly addresses the need for a multimodal chatbot that can both answer questions and generate visuals. GPT-4 excels at natural language understanding and generation, making it ideal for conversational responses, while DALL-E is purpose-built to create images from textual descriptions, such as generating a product prototype on demand. On the AI-900 exam, this question tests your grasp of Azure’s generative AI model specializations—a common trap is confusing DALL-E with a text-only model or selecting a single model that cannot handle both modalities. Remember the pairing as “talk and draw”: GPT-4 handles the talking, DALL-E handles the drawing. A useful memory tip is to think of GPT-4 as the “writer” and DALL-E as the “illustrator” in your multimodal toolkit.
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.
A company is developing a chatbot that can both answer customer questions in natural language and create images on demand (e.g., 'Generate a picture of a product prototype'). Which combination of Azure generative AI models should they integrate?
Correct. GPT-4 handles conversational text, and DALL-E generates images from text prompts, making this the ideal combination for the described chatbot.
B
B. GPT-3 for text and Custom Vision for images
Why wrong: Custom Vision is used for image classification and object detection, not for generating new images from text.
C
C. BERT for text and OCR for images
Why wrong: BERT is a language model for understanding (not generation), and OCR extracts text from images, not image generation.
D
D. Language Understanding (LUIS) and Face API
Why wrong: LUIS extracts intents and entities from user input, and Face API analyzes faces in images; neither generates text or images.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A. GPT-4 for text and DALL-E for images
Option A is correct because GPT-4 is a generative AI model optimized for natural language understanding and generation, making it ideal for answering customer questions in a conversational manner. DALL-E is a generative AI model specifically designed to create images from textual descriptions, enabling the chatbot to generate product prototypes on demand. Together, they cover both text and image generation requirements.
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.
✓
A. GPT-4 for text and DALL-E for images
Why this is correct
Correct. GPT-4 handles conversational text, and DALL-E generates images from text prompts, making this the ideal combination for the described chatbot.
Related concept
Read the scenario before looking for a memorised answer.
✗
B. GPT-3 for text and Custom Vision for images
Why it's wrong here
Custom Vision is used for image classification and object detection, not for generating new images from text.
✗
C. BERT for text and OCR for images
Why it's wrong here
BERT is a language model for understanding (not generation), and OCR extracts text from images, not image generation.
✗
D. Language Understanding (LUIS) and Face API
Why it's wrong here
LUIS extracts intents and entities from user input, and Face API analyzes faces in images; neither generates text or images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Custom Vision (a classification/detection service) with a generative image model, or assume older models like GPT-3 or BERT are sufficient for generative tasks, when in fact only GPT-4 and DALL-E are purpose-built for generative text and image creation respectively.
Detailed technical explanation
How to think about this question
GPT-4 uses a transformer-based decoder architecture with autoregressive text generation, enabling it to maintain context over multi-turn conversations. DALL-E employs a diffusion model that iteratively refines random noise into a coherent image guided by a text embedding, allowing fine-grained control over visual attributes like style and composition. In practice, integrating these models requires careful prompt engineering to ensure the chatbot correctly routes image requests to DALL-E while using GPT-4 for conversational flow.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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. GPT-4 for text and DALL-E for images — Option A is correct because GPT-4 is a generative AI model optimized for natural language understanding and generation, making it ideal for answering customer questions in a conversational manner. DALL-E is a generative AI model specifically designed to create images from textual descriptions, enabling the chatbot to generate product prototypes on demand. Together, they cover both text and image generation requirements.
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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