AI-102 Plan and manage an Azure AI solution Practice Question
Exhibit
{
"pipeline": {
"steps": [
{
"step": "1",
"action": "OCR",
"source": "document"
},
{
"step": "2",
"action": "Layout extraction",
"source": "OCR output"
},
{
"step": "3",
"action": "Table extraction",
"source": "Layout output"
}
],
"result": {
"tables": []
}
}
}Refer to the exhibit. You are using Azure AI Document Intelligence with a layout model. The pipeline returns an empty tables array even though the document contains tables. The OCR step extracts text correctly. What is the most likely issue?
⚠ Common exam trap
Watch out — candidates often assume OCR and table extraction are the same step, but Azure AI Document Intelligence separates text recognition from structural layout analysis, so correct OCR does not guarantee correct table detection.
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
✓
The layout extraction step is not correctly identifying table structures.
The layout model in Azure AI Document Intelligence performs OCR and then uses a layout extraction step to identify structural elements like tables. If the OCR extracts text correctly but the tables array is empty, it indicates that the layout extraction step failed to detect the table boundaries or cell structure, not that OCR missed the text. Option D correctly identifies this as the most likely issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The OCR step is not recognizing table cells.
Why it's wrong here
OCR extracts characters and their coordinates; the layout model's table-detection logic infers rows and columns from those coordinates, so OCR cannot recognise cells as such. Cell recognition is a layout-model capability, relevant only when choosing a model that returns structured table cells rather than raw text lines.
- ✗
The table extraction step is misconfigured.
Why it's wrong here
The layout model performs table extraction internally with no separate configurable step, so nothing exists to misconfigure. A distinct extraction step appears in custom models, where you train and configure field extraction — the correct route when the built-in layout output does not meet your schema needs.
- ✗
The output field mapping for tables is missing.
Why it's wrong here
The layout model returns tables automatically; no output field mapping exists to omit, so a missing mapping cannot empty the array. Field mapping belongs to custom extraction models, where you map labelled fields to schema properties — the right approach when building a bespoke model, not when reading a document's inherent table structure.
- ✓
The layout extraction step is not correctly identifying table structures.
Why this is correct
The layout model performs its own table-structure detection, separate from OCR text extraction. Correct text with an empty tables array means the structure-detection stage is failing to recognise rows, columns and spans, so the table identification step is the faulty component.
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