Pass the Microsoft Azure AI Engineer Associate AI-102 Questions and answers with ValidTests

For a table projection in a knowledge store (skillset definition), the table node must define:

tableName: the table to project into (physical expression).

generatedKeyName: the name of the system-generated key column.

source: an object-shaping expression (projection path) describing what document content flows into the table.dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.

References

Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.

You have an Azure AI Document Intelligence resource named AIDoc1 in the S0 tier.

You need to identify which files can be uploaded to Document Intelligence Studio for training a custom extraction model.

Supported formats: PDF, TIFF, JPG, PNG, BMP.

Maximum file size: 500 MB per document.

Password-protected documents: Not supported.

S0 tier limits: Same as above (50 pages per document limit also applies).

Now checking the given files:

File1 (JPG, 400 MB, No password) → Supported (under 500 MB, allowed format).

File2 (PDF, 250 MB, No password) → Supported.

File3 (PNG, 180 MB, Password-protected) → Not supported (password locked).

File4 (XLSX, 900 MB) → Not supported (format not supported, exceeds 500 MB).

File5 (PDF, 160 MB, Password-protected) → Not supported (password locked).

Correct Answer: C. File1, File2, and File4 only → Wait, check again: XLSX is not supported, so File4 is invalid. Only File1 and File2 are valid.

Correct Final Answer: A. File1, and File2 only

Platform as a service (PaaS) database offerings in Azure require less setup and configuration effort than Infrastructure as a Service (IaaS) database offerings.

Yes

PaaS abstracts away the OS, patching, backups, and much of the infrastructure management. IaaS requires users to configure and manage VMs and databases manually.

Platform as a service (PaaS) database offerings in Azure provide end users with the ability to control and update the operating system version.

No

In PaaS, the OS and platform management are handled by Azure. Users cannot directly control or update the OS version. This is a responsibility difference between IaaS and PaaS.

All relational and non-relational platform as a service (PaaS) database offerings in Azure can be paused to reduce costs.

No

Not all PaaS database services support pausing. For example, Azure SQL Database serverless can auto-pause, but Azure Cosmos DB and provisioned SQL databases cannot. Hence this statement is incorrect.

Yes

No

No

Correct Responses:

Azure SQL Database – PaaS overview

What is PaaS?

Serverless compute tier in Azure SQL Database (auto-pause)

Microsoft References

Scenario RecapYou are building a streaming Speech-to-Text solution using the Speech SDK.

Input audio format: MP3 encoding

You must configure the audio stream format and the recognizer properly.

Audio Format

For MP3 streams, the correct method is:

AudioStreamFormat.GetCompressedFormat(AudioStreamContainerFormat.MP3)

This creates an audio format object for compressed MP3 input.

Recognizer

Since the task is speech-to-text transcription, you need the SpeechRecognizer class.

Other options like KeywordRecognizer, SpeakerRecognizer, or SpeechSynthesizer are not appropriate (they handle keyword spotting, speaker identification, or text-to-speech).

Code Analysis

Completed Code Snippetvar audioFormat = AudioStreamFormat.GetCompressedFormat(AudioStreamContainerFormat.MP3);

var speechConfig = SpeechConfig.FromSubscription("18c51a87-3a69-47a8-aedc-a54745f708a1", "westus");

var audioConfig = AudioConfig.FromStreamInput(pushStream, audioFormat);

using (var recognizer = new SpeechRecognizer(speechConfig, audioConfig))

{

var result = await recognizer.RecognizeOnceAsync();

var text = result.Text;

}

AudioFormat: AudioStreamFormat.GetCompressedFormat

Recognizer: SpeechRecognizer

Final Answer (Answer Area Selections)

Speech SDK – AudioStreamFormat

SpeechRecognizer class

Microsoft References

For an Azure AI Language Custom Question Answering project (QA1), you can import Q&A pairs directly from Excel (.xls/.xlsx) or TSV files in Language Studio. Microsoft’s guidance explicitly states that when importing question–answer sources into a project, you can choose Excel or TSV. JSON, LU, and CSV are not supported as import file types for Q&A pairs in Language Studio. Microsoft Learn

Additional Microsoft guidance notes that structured TXT, TSV, and XLS files can be uploaded to create or augment a project, and clarifies import/export behavior for TSV and XLS. This reinforces that Excel and TSV are the supported formats here. Microsoft Learn

Microsoft References:

Move projects and Q&A pairs – “Import question and answers… option to import either an Excel or TSV file.” Microsoft Learn

Format guidelines for Custom Question Answering – structured TXT, TSV, and XLS supported; import/export notes for TSV and XLS. Microsoft Learn

You’re indexing content with Azure AI Search and you want to recognize and retrieve properties from invoices (such as invoice number, vendor, dates, totals, line items). The Azure service designed for structured information extraction from forms and documents is Azure AI Document Intelligence (formerly Form Recognizer).

In an Azure AI Search enrichment pipeline, you add a skillset. To pull structured fields from invoices, include a skill that invokes Document Intelligence’s prebuilt Invoice model. This model already extracts common invoice fields, so you don’t have to train anything—meeting the goal of minimal effort. You can wire this in using the built-in Document Intelligence skill for Search, or as a custom Web API skill that calls the Document Intelligence REST API, and then map the extracted fields into your index for retrieval and filtering.

Why the others are not suitable:

Azure OpenAI is for generative/LLM tasks, not schema-level invoice field extraction.

Azure AI Immersive Reader improves reading accessibility; it doesn’t extract business fields.

Azure Custom Vision is for image classification/object detection, not form/key-value extraction.

Microsoft References (Azure AI Solution)

Azure AI Search skillsets – add AI enrichment and custom skills (Web API skill).

Document Intelligence skill for Azure AI Search (formerly Form Recognizer skill) – integrate extraction into indexing.

Prebuilt Invoice model – fields extracted (vendor, invoice ID/date, totals, taxes, line items, etc.).

Azure AI Document Intelligence overview – key-value extraction and table/line-item parsing from documents.

PaaS databases reduce administrative overhead because Microsoft manages backups, patching, updates, and infrastructure.

Users always get access to the latest features without manual upgrades.

B is incorrect: Backup/restore is automated; you don’t get full manual control.

C is incorrect: In-database ML services are supported in SQL Server (on-prem / IaaS), but not natively in Azure SQL Database.

The scenario involves storing text files, videos, audio streams, and virtual disk images, along with metadata and a unique ID. This exactly matches Object storage characteristics:

Stores large amounts of unstructured data (blobs).

Each object contains the data itself, metadata, and a unique identifier.

Azure Blob Storage is Microsoft’s object storage solution.

Other options:

Columnar → Used for analytics workloads, optimized for reading columns (e.g., data warehouses).

Key/Value → Good for simple lookups but not optimized for storing large multimedia files.

Document → Stores semi-structured JSON documents, not large binary objects.

Thus, the correct answer is Object storage.

Microsoft Reference: Azure Blob Storage (Object storage) overview

Model Used: "modelId": "prebuilt-healthInsuranceCard.us"

The request was analyzing Blood Pressure 118/72, which is not a health insurance card.

This model is not suitable for the intended medical use case (vital signs).

Text Recognition Confidence:

"Blood" → confidence: 0.766

"Pressure" → confidence: 0.716

"118/72" → confidence: 0.761

All recognized words are above 0.70 (70%).

So, text recognition was successful above the threshold.

Form Elements (fields):

"fields": {} → empty (no extracted fields).

Confidence for fields = 1, but since no fields were extracted, we cannot consider that form elements were recognized.

Therefore, form extraction was not achieved.

JSON Key Observations

The chosen model is suitable for the intended use case. → No

The text content was recognized with greater than 70 percent confidence. → Yes

The form elements were recognized with greater than 70 percent confidence. → No

Correct Answers