AI-900 Practice Question: Describe features of computer vision 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.

• ✗

  Azure AI Custom Vision with medical training data

  Why it's wrong here

  Azure AI Custom Vision is a general-purpose image classification and object detection service that can be trained on any labeled image set, including medical images. However, it lacks DICOM support, clinical workflow integration, healthcare-specific output schemas, and regulatory/clinical validation, meaning a team would have to build and validate the entire pipeline themselves. It is a viable research prototyping tool, but it is not a specialized clinical imaging AI and is therefore less suitable than Azure AI Health Insights.

• ✓

  Azure AI Health Insights and specialized medical imaging AI

  Why this is correct

  Azure AI Health Insights is purpose-built for healthcare workloads, bringing together capabilities such as Radiology Insights and clinical text analysis to support clinical decision-making. It integrates with medical imaging data and EHR/FHIR systems, enabling specialized analysis of imaging studies alongside other health data. This combination of healthcare domain knowledge, DICOM-aware processing, and clinical validation makes it the appropriate choice for medical imaging analysis, unlike general-purpose audio, text, or image services.

• ✗

  Azure AI Translator for medical documents

  Why it's wrong here

  Azure AI Translator performs neural machine translation on textual content, including medical documents and clinical notes. It operates exclusively on text and has no mechanism for reading pixel data or extracting anatomical findings from CT, MRI, or X-ray images. Therefore, translating a radiology report does not equate to analyzing the medical image; that requires vision and healthcare-specific AI.

• ✗

  Azure AI Speech for dictation only

  Why it's wrong here

  Azure AI Speech is a speech-to-text and text-to-speech service optimized for transcribing dictation, meetings, and audio content. It has no image-processing pipeline and cannot ingest, encode, or interpret radiological images, so using it for medical imaging would only capture a clinician's spoken notes rather than analyze the actual image. The clinical image itself requires computer vision capabilities, not speech recognition.

Go deeper

Related to this question