Free Microsoft AI-102 Practice Exam with Questions & Answers | Set: 3

Box 1: api-nam.cognitive.microsofttranslator.com

https://docs.microsoft.com/en-us/azure/cognitive-services/translator/referenc e/v3-0-reference

Box 2: /translate

Box 1: Azure Blob storage

At the start of the pipeline, you have unstructured text or non-text content (such as images, scanned documents, or JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.

Box 2: Computer Vision API

Scenario: Provide users with the ability to search insight gained from the images, manuals, and videos associated with the products.

The Computer Vision Read API is Azure's latest OCR technology (learn what's new) that extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents.

Box 3: Translator API

Scenario: Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.

Box 4: Azure Files

Scenario: Store all raw insight data that was generated, so the data can be processed later.

Scenario: Support autocompletion and autosuggestion based on all product name variants.

A: Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API usage is illustrated in the following call to the Autocomplete REST API.

POST /indexes/myxboxgames/docs/autocomplete?search&api-version=2020-06-30

{

"search": "minecraf",

"suggesterName": "sg"

}

B: In Azure Cognitive Search, typeahead or "search-as-you-type" is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for "Seattle" will have prefix combinations of "sea", "seat", "seatt", and "seattl" to support typeahead.

F. Use the default standard Lucene analyzer ("analyzer": null) or a language analyzer (for example, "analyzer": "en.Microsoft") on the field.

Comprehensive Detailed ExplanationYou’re adding multiple images for each person into a PersonGroup using the Face API SDK.

File.OpenRead(imagePath) returns a Stream, so the using declaration must declare Stream t = File.OpenRead(imagePath).

To upload an image from a stream to a person in a person group, callfaceClient.PersonGroupPerson.AddFaceFromStreamAsync(personGroupId, personId, t);

Putting it together:

Parallel.For(0, PersonCount, async i =>

{

Guid personId = persons[i].PersonId;

string personImageDir = $"/path/to/person/{i}/images";

foreach (string imagePath in Directory.GetFiles(personImageDir, "*.jpg"))

{

using (Stream t = File.OpenRead(imagePath))

{

await faceClient.PersonGroupPerson.AddFaceFromStreamAsync(

personGroupId, personId, t);

}

}

});

This loops the images for each person, opens each as a stream, and adds the face to the specified person in the person group.

Microsoft Docs – Face API: Add a face to a person (AddFaceFromStreamAsync, AddFaceFromUrlAsync) in PersonGroupPerson operations.https://learn.microsoft.com/azure/ai-services/face/ (see SDK/PersonGroupPerson methods)

.NET File.OpenRead returns Stream.https://learn.microsoft.com/dotnet/api/system.io.file.openread

References

Parameter type: stream

Visual features list: VisualFeatureTypes.Description

Result to use: results.Description.Captions[0]

To generate accessible alt text, you should use the caption produced by Azure Computer Vision’s Description feature (it produces a human-readable sentence with a confidence score). Therefore:

The image input should be a stream, because you’re uploading images (not just passing URLs) during product creation and AnalyzeImage…Async supports image streams.

Request the Description feature in VisualFeatureTypes so the service returns results.Description.Captions.

Use results.Description.Captions[0] and return the caption text if its confidence is high enough (e.g., > 0.5) to meet the accessibility requirement that all images must have relevant alt text.

Other features (Tags, Objects, Brands) are useful for enrichment but do not directly return natural-language captions suitable for alt text.

Microsoft Azure AI Solution References

Computer Vision (Image Analysis) – Description/Captions and features: Microsoft Docs, Image Analysis – VisualFeatureTypes.Description returns description.captions.https://learn.microsoft.com/azure/ai-services/computer-vision/concept-image-analysis

SDK usage (analyze image from a stream): Microsoft Docs, Analyze an image by using the Computer Vision client library.https://learn.microsoft.com/azure/ai-services/computer-visi on/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

The D in ACID stands for Durability.

Durability guarantees that once a transaction is committed, the changes are permanent, even in the case of power loss, crashes, or OS restarts.

Atomicity = all-or-nothing.

Consistency = valid state transitions.

Isolation = transactions run independently without interference.

Correct Answer: A. durability

When deploying an Azure OpenAI model using the Microsoft.CognitiveServices/accounts/deployments resource in an ARM template, the throughput for a Standard deployment is set on the SKU object. Specifically:

Use the capacity field of the SKU to specify the per-deployment rate.

Provide the integer value that represents the requests per minute (RPM) you want the deployment to handle.

Therefore, to meet the requirement of 600 requests per minute, complete the template as:

"sku": {

"name": "Standard",

"capacity": 600

}

This uses a single deployment SKU with a defined capacity. The ARM schema for accounts/deployments explicitly includes sku.capacity for resources that support scale (Azure OpenAI deployments do), and Azure OpenAI quota/rate-limit guidance confirms that rate limits are assigned per deployment.

Microsoft Azure AI Solution References

ARM template—Microsoft.CognitiveServices/accounts/deployments (2023-05-01): property reference showing sku.capacity. Microsoft Learn

Resource type reference—latest accounts/deployments: confirms SKU usage on deployments. Microsoft Learn

Azure OpenAI quota and rate limits: explains assigning deployment-level limits (RPM/TPM). Microsoft Learn

Comprehensive Detailed ExplanationThe question asks about testing a bot interactively on a local machine when using the Microsoft Bot Framework SDK.

Steps to test locally:

Build and run the bot

Compile the bot application code and run it locally (e.g., using Visual Studio or Node.js).

This exposes the bot’s endpoint on localhost (commonly http://localhost:3978/api/messages ).

Open the Bot Framework Emulator

The Bot Framework Emulator is a desktop app for testing and debugging bots built with the Bot Framework SDK.

It allows you to send messages to the bot and see the responses.

Connect to the bot endpoint

Use the Emulator to connect to the locally running bot by entering its endpoint URL.

Once connected, you can interact with the bot as if you were a real user.

Register the bot with the Azure Bot Service → Needed for cloud deployment, not local testing.

Open the Bot Framework Composer → Only needed if designing the bot with Composer, but this scenario uses the SDK directly.

Actions Not Required in This Case

Build and run the bot → Open the Bot Framework Emulator → Connect to the bot endpoint

Correct Sequences (multiple possible valid orders):

Test a bot locally with Bot Framework Emulator

Bot Framework SDK overview

Microsoft References

✅ Final Answer Sequence:

Build and run the bot

Open the Bot Framework Emulator

Connect to the bot endpoint

https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/custom-speech-overview #how-does-it-work

With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint.

- Create a project and choose a model. Use a Speech resource that you create in the Azure portal. If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data.

- Upload test data. Upload test data to evaluate the speech to text offering for your applications, tools, and products.

- Train a model. Provide written transcripts and related text, along with the corresponding audio data. Testing a model before and after training is optional but recommended.

- Deploy a model. Once you're satisfied with the test results, deploy the model to a custom endpoint. With the exception of batch transcription, you must deploy a custom endpoint to use a Custom Speech model.

Within a search service, synonym maps are a global resource that associate equivalent terms, expanding the scope of a query without the user having to actually provide the term. For example, assuming "dog", "canine", and "puppy" are mapped synonyms, a query on "canine" will match on a document containing "dog".

Create synonyms: A synonym map is an asset that can be created once and used by many indexes.