- A
Use the synchronous API of the prebuilt invoice model. For each invoice, call the API and write the result to Cosmos DB.
Why wrong: Synchronous API is not designed for high throughput; async is better for volume.
- B
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.
Async API handles high volume; Azure Function automates the workflow.
- C
Use the prebuilt receipt model to process invoices. Store results in Cosmos DB.
Why wrong: Receipt model is not optimized for invoice fields.
- D
Use the layout model to extract text from invoices. Then use Azure AI Language to extract entities.
Why wrong: This is more complex and less accurate than using the prebuilt invoice model.
Quick Answer
The answer is to use the prebuilt invoice model with the async API, submitting all invoices for analysis and then using an Azure Function to poll for results before writing to Cosmos DB. This approach is correct because the prebuilt invoice model’s asynchronous API is specifically designed for high-volume batch processing, handling up to 10,000 invoices per day without timeout or rate-limit issues, while the polling mechanism decouples submission from retrieval, ensuring scalability for both PDF and image formats. On the AI-102 exam, this scenario tests your understanding of when to choose async over sync APIs for Document Intelligence pipelines—a common trap is selecting the synchronous API, which fails under high throughput. Remember the key: for high volume, always go async and poll; for low volume, sync is fine. Memory tip: “Async for the batch, sync for the snap.”
AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. 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.
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?
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.
Option B is correct because 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.
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.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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
This is more complex and less accurate than using the prebuilt invoice model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
The async API for the prebuilt invoice model uses a two-step pattern: first, you POST the document to the `https://{endpoint}/formrecognizer/documentModels/prebuilt-invoice:analyze?api-version=2023-07-31` endpoint with `output=json` and receive an `Operation-Location` header; then you poll that URL with GET requests until the status is 'succeeded'. Under the hood, Azure AI Document Intelligence uses a transformer-based OCR engine that handles both PDF and image formats natively, and the async pipeline can process up to 500 pages per batch, making it ideal for high-throughput scenarios like 10,000 invoices per day.
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.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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. — Option B is correct because 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.
What should I do if I get this AI-102 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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Last reviewed: Jun 24, 2026
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