A logistics company processes shipping manifests that include a table of items with quantities, a sender address, and a recipient address. They need to extract the total weight from a specific field, the list of item names, and the recipient city. The manifests are in PDF format. What should they configure in Azure Content Understanding?

Use table extraction only to get the item table and then manually read the total weight and recipient city from the extracted table.

Create a custom field extraction pipeline with fields for total weight, recipient city, and a table field for item names, with OCR enabled.

Use document summarization to generate a summary that includes the total weight and recipient city.

Use entity extraction to identify all locations and then infer the recipient city from the list of locations.

A legal firm processes court rulings that vary in length and format. They need to classify each ruling as 'favorable' or 'unfavorable', extract the judge's name and the ruling date, and generate a summary. They have a constraint: the pipeline must handle both short and long documents without truncation, and they want to minimize the number of pipeline runs. What should they do?

Create a single pipeline with OCR and entity extraction, and manually classify the rulings.

Create separate pipelines for short and long documents, each with classification, entity extraction, and summarization.

Create a single pipeline with only entity extraction and summarization, and infer the classification from the summary.

Create a single custom pipeline with document classification, entity extraction, and summarization, and configure it to handle long documents.

A news agency receives press releases from various sources. They need to categorize each release as 'urgent' or 'routine', extract the event date and location, and generate a brief summary for editors. They want to automate this using Azure Content Understanding in Foundry Tools. What should they create?

An attribute detection pipeline that extracts date and location and classifies urgency.

A table extraction pipeline to pull the date and location from any tables in the press release.

A custom pipeline with document classification, entity extraction for date and location, and summarization.

An OCR pipeline to read the text and then manually categorize and summarize.

A bank receives loan applications in two formats: a structured form with a table of assets and a free-form cover letter. The bank needs to classify each application as 'complete' or 'incomplete' based on whether the asset table is present and the cover letter includes a signature. They want to use Azure Content Understanding in Foundry Tools, but they have a constraint: the pipeline must process both formats in a single run and minimize the number of custom fields they need to define. What should they do?

Create a single pipeline with only document classification and table extraction, and infer the signature from the table.

Create two separate pipelines: one for the structured form and one for the cover letter, each with its own classification and extraction.

Create a single pipeline with OCR and entity extraction for the signature, and classify based on the extracted text.

Create a single custom pipeline with document classification, table extraction, and attribute detection for the signature.

Microsoft Certified:Azure AI Engineer Associate

Domain 6Objective 3

Extract Information with Azure Content Understanding in Foundry Tools AI-102 Practice Questions (Page 5)

Part of the Implement knowledge mining and information extraction solutions domain, which accounts for 15-20% of the AI-102 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~6–13 in this domain), expect 2–4 from this objective — we provide 47 practice questions to prepare you well beyond it. (estimate)

47questions here

10free pages

8concepts

15-20%of the exam

Questions 21–25

21foundation · easy
Which Azure Content Understanding capability would you use to pull out all the person names mentioned in a contract?
Select an answer first
22application · medium
A logistics company processes shipping manifests that include a table of items with quantities, a sender address, and a recipient address. They need to extract the total weight from a specific field, the list of item names, and the recipient city. The manifests are in PDF format. What should they configure in Azure Content Understanding?
Select an answer first
23expert · hard
A legal firm processes court rulings that vary in length and format. They need to classify each ruling as 'favorable' or 'unfavorable', extract the judge's name and the ruling date, and generate a summary. They have a constraint: the pipeline must handle both short and long documents without truncation, and they want to minimize the number of pipeline runs. What should they do?
Select an answer first
24application · medium
A news agency receives press releases from various sources. They need to categorize each release as 'urgent' or 'routine', extract the event date and location, and generate a brief summary for editors. They want to automate this using Azure Content Understanding in Foundry Tools. What should they create?
Select an answer first
25expert · hard
A bank receives loan applications in two formats: a structured form with a table of assets and a free-form cover letter. The bank needs to classify each application as 'complete' or 'incomplete' based on whether the asset table is present and the cover letter includes a signature. They want to use Azure Content Understanding in Foundry Tools, but they have a constraint: the pipeline must process both formats in a single run and minimize the number of custom fields they need to define. What should they do?
Select an answer first

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Extract Information with Azure Content Understanding in Foundry Tools AI-102 Practice Questions (Page 5)

Questions 21–25

Which Azure Content Understanding capability would you use to pull out all the person names mentioned in a contract?

Keep studying — Implement knowledge mining and information extraction solutions