Free Microsoft AI-102 Practice Exam with Questions & Answers

When using Azure Translator REST API, required headers are:

Ocp-Apim-Subscription-Key → subscription key

Ocp-Apim-Subscription-Region → resource region (if using a multi-service Cognitive Services resource)

Content-Type → typically application/json or application/xml

Other options:

Client trace ID is optional for debugging.

Resource ID, access control request, content language, content length are not required headers.

Correct Answer: B

This is because the Azure Video Indexer website allows you to upload videos from a URL or from your file system, but there are some limitations and considerations for each option1.

If you upload from your file system, the size of the file is limited to 2 GB, which is less than the size of file1.avi (20 GB). Therefore, this option is not feasible.

If you upload from a URL, the size of the file is limited to 30 GB, which is enough for file1.avi. However, the URL must be publicly accessible and valid, and the file must be accessible. You cannot use URLs from streaming services such as YouTube1. Therefore, options A and B are not valid.

The best option is to upload file1.avi to a cloud storage service such as Microsoft OneDrive, and then generate a shareable link for the file. You can then paste the link in the Azure Video Indexer website and upload the video from the URL. This way, you can avoid the file size limitation and ensure that the file is accessible and valid2.

To present a list of options with images, you use Adaptive Cards, which allow for rich content such as images, buttons, and formatted text in the conversation.

These cards are typically surfaced within the context of a dialog, which controls the flow of the conversation.

Azure functions are unrelated here.

Entities and utterances belong to natural language understanding, not UI presentation.

Correct Answer: B and D

 HTTP Method: POST

 Visual Feature: imageType

Comprehensive Detailed Explanation

The question requires configuring an Azure AI Vision API request that determines if an image is clipart or a line drawing.

Step 1 – Determine the HTTP Method

The Analyze Image endpoint ( /analyze ) of Azure AI Vision expects an HTTP POST request.

GET is not valid for analysis with an image body.

PATCH is irrelevant here.

POST is correct because you are submitting image content or a URL for analysis.

On-premises Microsoft SQL Server database cannot be used as an index data source.

Note: Indexer in Azure Cognitive Search: : Automate aspects of an indexing operation by configuring a data source and an indexer that you can schedule or run on demand. This feature is supported for a limited number of data source types on Azure.

Indexers crawl data stores on Azure.

Azure Blob Storage

Azure Data Lake Storage Gen2 (in preview)

Azure Table Storage

Azure Cosmos DB

Azure SQL Database

SQL Managed Instance

SQL Server on Azure Virtual Machines

Comprehensive Detailed Explanation

A custom neural voice (professional voice) project in Azure AI Speech requires that you follow Responsible AI and consent steps before any model training. The correct workflow in Speech Studio / Azure AI Foundry is:

Create a custom voice project – This is the container that will hold the voice talent consent, your training datasets, models, and deployments. Each project is scoped to a locale and other attributes. Microsoft Learn

Collect and upload consent – Microsoft requires explicit authorization from the voice talent. In practice, you add the voice talent consent to the project. The platform requires the verbal consent recording (WAV); many guidance and exam items also include the signed consent document (PDF) as part of consent capture to meet Responsible AI expectations. The consent step is performed before uploading training data.

Upload the voice talent’s verbal consent as a WAV file – The consent audio verifies that the person who granted consent is the same as the speaker in your training data. The documentation shows consent is a recorded statement uploaded to the project. Microsoft Learn

Analyze the quality of the audio data and fix issues – After data upload, Speech Studio runs validation and provides a per-utterance analysis (format checks, sampling rate, script match, pronunciation/noise scoring). You must resolve issues and run Analyze data before the dataset can be used for training. Microsoft Learn

Train the model using a neural training method – Start model training from the project and choose Neural as the training type for custom neural voice. Microsoft Learn

Why not “upload speech samples as WMA/MP3 files”?

For the standard, recommended Individual utterances + matching transcript data type used to fine-tune a professional neural voice, the audio files must be WAV/RIFF PCM; the docs explicitly reference “.wav” throughout data checks and corrections. WMA isn’t supported, and MP3 isn’t accepted for this core data type. (Long audio / audio-only workflows can accept MP3 for preprocessing, but the canonical training dataset for custom neural voice is WAV.)

Microsoft Azure AI References

Create a project for professional voice (Speech Studio/Azure AI Foundry): Project contains consent, datasets, models, endpoints. Microsoft Learn

Add voice talent consent to the professional voice project: Upload the recorded consent statement for the voice talent. Microsoft Learn

Professional voice fine-tuning data – required formats: Individual utterances + transcript require WAV/RIFF PCM; guidance on sample rate and transcript format. Microsoft Learn

Add a professional voice training dataset & resolve data issues: Data validation, pronunciation/noise checks, and the Analyze data step prior to training. Microsoft Le arn

Train your professional voice model: Start training and select Neural method. Microsoft Learn

The requirement is to optimize the reading experience for users with dyslexia and learning differences .

Azure AI Immersive Reader is specifically designed to make text more accessible, improving comprehension by enabling features like text-to-speech, line focus, and translation.

Translator (A) is for translating text, not accessibility.

Document Intelligence (B) is for extracting information from documents.

Azure AI Language (D) is for NLP tasks, not improving reading accessibility.

Microsoft References:

Azure AI Immersive Reader overview

The code snippet shows a helper function provision_resource that provisions Azure Cognitive Services accounts by specifying:

Name (resource name)

Kind (the type of Cognitive Service, e.g., ComputerVision, FormRecognizer, etc.)

Location (Azure region, such as eastus)

SKU tier (pricing tier, e.g., F0 = free, S0 = standard)

Step 1 – Identify the service

The dropdown includes ComputerVision , CustomVision.Prediction , CustomVision.Training , and FormRecognizer .

The question context indicates a need to provision an Azure AI service resource, and the answer area highlights the FormRecognizer option. This aligns with document processing scenarios requiring Azure AI Document Intelligence (formerly Form Recognizer).

Step 2 – Select pricing tier and location

The second dropdown shows combinations of pricing and regions:

" eastus " , " S1 "

" useast " , " S1 "

" S0 " , " eastus "

" S0 " , " useast "

Azure AI Form Recognizer (Document Intelligence) supports S0 (Standard) and F0 (Free) SKUs in common regions like East US. " S0 " , " eastus " is a valid and recommended configuration.

Other options like " S1 " are not correct here, as Form Recognizer uses S0/F0, not S1.

Correct Answer:

Service Kind: FormRecognizer

Location and SKU: " S0 " , " eastus "

Microsoft References

Azure AI Document Intelligence (Form Recognizer) documentation

Azure Cognitive Services resource creation parameters

Pricing tiers for Form Recognizer