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Plan and manage an Azure AI solutionmediumMultiple ChoiceObjective-mapped

AI-102 Plan and manage an Azure AI solution Practice Question

A financial services company uses Azure AI Document Intelligence to process loan applications. The solution extracts data from uploaded PDFs and stores it in Azure Cosmos DB. Recently, extraction errors increased due to poor-quality scans. The company needs to improve accuracy without manual intervention. The solution must also handle varying document layouts. You need to recommend a plan. What should you do?

⚠ Common exam trap

Many candidates confuse throughput scaling (Option A) or generic OCR enhancement (Option B) with actual model improvement, overlooking that only custom training can adapt to specific data quality issues and layout variations.

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

Train a custom document extraction model using labeled samples of poor-quality scans and different layouts.

Training a custom document extraction model using labeled samples of poor-quality scans and varying layouts directly addresses the root cause of extraction errors. Azure AI Document Intelligence's custom model capability allows you to train on specific document types and quality issues, improving accuracy without manual intervention. This approach adapts to the company's need to handle diverse layouts and degraded image quality, which prebuilt models cannot reliably manage.

Answer analysis

Option-by-option breakdown

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

  • Increase the throughput (TPS) of the Document Intelligence resource.

    Why it's wrong here

    Throughput affects performance, not accuracy.

  • Enable OCR enhancement in Document Intelligence.

    Why it's wrong here

    OCR is already part of Document Intelligence; the issue is extraction accuracy, not OCR.

  • Switch to Azure AI Language for entity extraction.

    Why it's wrong here

    Language is for text analytics, not document layout extraction.

  • Train a custom document extraction model using labeled samples of poor-quality scans and different layouts.

    Why this is correct

    Custom models learn from specific examples, improving accuracy on varied layouts and quality.

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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.