AI-102 Plan and manage an Azure AI solution Practice Question
Your company is deploying an Azure AI Document Intelligence solution to process invoices. The solution must: - Extract key fields (invoice number, date, total amount). - Handle invoices in both PDF and image formats. - Use a prebuilt model to reduce development effort. - Process high volumes (up to 10,000 invoices per day). - Store extracted data in Azure Cosmos DB. You need to design the processing pipeline. What should you do?
⚠ Common exam trap
It's easy for candidates to assume the synchronous API is simpler and sufficient for high volume, but they overlook the rate limits and payload constraints that make the async API mandatory for production-scale invoice processing.
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
✓
Use the prebuilt invoice model with the async API. Submit all invoices for analysis. Use an Azure Function to poll for results and write to Cosmos DB.
The prebuilt invoice model's asynchronous API is designed for high-volume batch processing, allowing you to submit up to 10,000 invoices per day without timeout or rate-limit issues. The async API returns operation locations that you can poll via an Azure Function, and once results are ready, you write the extracted fields (invoice number, date, total amount) to Azure Cosmos DB. This decouples submission from retrieval, ensuring scalability and reliability for both PDF and image formats.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the synchronous API of the prebuilt invoice model. For each invoice, call the API and write the result to Cosmos DB.
Why it's wrong here
Synchronous API is not designed for high throughput; async is better for volume.
- ✓
Use the prebuilt invoice model with the async API. Submit all invoices for analysis. Use an Azure Function to poll for results and write to Cosmos DB.
Why this is correct
Async API handles high volume; Azure Function automates the workflow.
- ✗
Use the prebuilt receipt model to process invoices. Store results in Cosmos DB.
Why it's wrong here
Receipt model is not optimized for invoice fields.
- ✗
Use the layout model to extract text from invoices. Then use Azure AI Language to extract entities.
Why it's wrong here
Using the layout model for text extraction followed by Azure AI Language for entity extraction adds an unnecessary two-step pipeline that lacks a prebuilt model purpose-built for invoices. The Document Intelligence prebuilt invoice model directly extracts fields like invoice number and total amount in a single call, whereas the layout model only returns raw text and spatial coordinates without semantic field mapping. This option is tempting because Azure AI Language’s entity extraction is effective for general-purpose named entity recognition from unstructured text, making it a correct choice for scenarios like extracting entities from free-form documents or emails where no specialised prebuilt model exists.
Go deeper
Related to this question
About these practice questions
Courseiva writes every AI-102 question from scratch — 945 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 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-102 exam.