AI-102 Exam Dumps - Microsoft Azure AI Engineer Associate Questions and Answers

Comprehensive Detailed Explanation

You have an Azure Cognitive Search service. Query volume has increased, and some search requests are now being throttled. This means your current tier is no longer sufficient to handle the query traffic.

Why does migrating to a higher tier work?

Each Azure Cognitive Search pricing tier (Basic, Standard S1/S2/S3, Storage Optimized, etc.) provides different resource limits for queries per second (QPS), indexing throughput, and storage.

Throttling occurs when query traffic exceeds the capacity limits of the current tier.

Moving to a higher tier increases:

The allowed query units (QUs),

Maximum queries per second (QPS),

and available compute resources.

Therefore, migrating to a higher tier reduces the likelihood of throttling and supports the increased query volume.

Why not other solutions?

Simply adding replicas can help scale out query workloads, but the question specifically asks whether moving to a higher tier meets the goal—and it does.

Using indexer scaling or adjusting query patterns might help, but they are not direct answers to the throttling caused by insufficient service tier capacity.

Correct Answer: A. Yes

Microsoft References

Azure Cognitive Search service limits by tier

Scale resources in Azure Cognitive Search

Azure Cognitive Search pricing tiers

Box 1: Cognitive Service User

Ensure that the members of a group named Management-Accountants can approve the FAQs.

Approve=publish.

Cognitive Service User (read/write/publish): API permissions: All access to Cognitive Services resource except for ability to:

1. Add new members to roles.

2. Create new resources.

Box 2: Cognitive Services QnA Maker Editor

Ensure that the members of a group named Consultant-Accountants can create and amend the FAQs.

QnA Maker Editor: API permissions:

1. Create KB API

2. Update KB API

3. Replace KB API

4. Replace Alterations

5. " Train API " [in new service model v5]

Box 3: Cognitive Services QnA Maker Read

Ensure that the members of a group named the Agent-CustomerServices can browse the FAQs.

QnA Maker Read: API Permissions:

1. Download KB API

2. List KBs for user API

3. Get Knowledge base details

4. Download Alterations

Generate Answer

The scenario involves a Language Understanding (LUIS) or Conversational Language Understanding (CLU) model that has:

An intent called FindContact (used for searching a contact list).

Training phrases like:

“Find contacts in London”

“Who do I know in Seattle?”

“Search for contacts in Ukraine”

Key Requirement

The model must recognize locations (London, Seattle, Ukraine) inside the FindContact intent.

Why the Solution is Incorrect

The proposed solution is to create a new intent for location.

This is not correct because:

Intents represent what the user wants to do (the action, e.g., FindContact ).

Entities represent details within the user’s utterance (the data, e.g., location ).

Here, the intent is already defined: FindContact .

Location should be modeled as an entity (Location entity), not as a separate intent.

Creating a new intent for each location (London, Seattle, Ukraine) would fragment the model and make it harder to generalize. Instead, the model should capture location as a parameter of the single intent FindContact .

Correct Approach

Keep the existing FindContact intent.

Add an entity for location (prebuilt GeographyV2 in LUIS or “Location” entity in CLU).

Train the model so that London , Seattle , Ukraine are recognized as entity values inside the FindContact intent.

Correct Answer: B. No

Microsoft References

Intents and entities in Language Understanding

Prebuilt entity: GeographyV2

The requirement is to process images of forms with Azure Form Recognizer (now part of Azure AI Document Intelligence) while ensuring that the image files are not stored in the cloud.

When sending images directly to the Form Recognizer REST API endpoint, you have two options:

Send the file as binary data in the request body (for local file upload).

Send a URL reference (if the file is stored in Azure Blob Storage or another public location).

Since the requirement explicitly states that the image files must not be stored in the cloud, you cannot send a URL. Instead, you must send the raw binary content of the file directly in the request body.

Option A – raw image binary

Correct. This allows you to directly send the local image file to the API without uploading/storing it in the cloud.

Option B – form URL encoded

Incorrect. Form Recognizer does not accept URL-encoded form data.

Option C – JSON

Incorrect. JSON is only used when providing the API with the URL of a cloud-stored document, not when sending the actual file.

Correct Answer: A. raw image binary

Microsoft References

Form Recognizer REST API – Analyze a document

Quickstart: Extract layout and text – Document Intelligence

" If your document is available locally, pass the file as binary data in the request body. "

You are securing an Azure Cognitive Search resource with two specific requirements:

Prevent access to Search1 from the internet

The correct way is to use a private endpoint.

A private endpoint connects the Cognitive Search resource directly into your virtual network (VNet), blocking public internet access.

Configuring an IP firewall can restrict access to specific IP ranges but does not fully prevent internet access.

Azure roles and authentication do not address network-level restrictions.

Limit the access of each app to specific queries

This is done via Azure role-based access control (RBAC).

You can assign different Azure roles or API keys with query rights to control what each app can do.

Key authentication alone only provides coarse-grained access (admin/query keys) but does not allow fine-grained per-app restrictions.

RBAC ensures principle of least privilege is enforced.

Correct Answer:

Prevent access from internet → Create a private endpoint

Limit access to queries → Use Azure roles

Microsoft References

Secure access to Azure Cognitive Search using private endpoints

Role-based access control in Azure Cognitive Search

The output will include the following words: our and included. No

The output will include the following words: Paris, Eiffel, and Tower. Yes

The function will output all the key phrases from the input string to the console. No

The output will include the following words: our and included. No

The output will include the following words: Paris, Eiffel, and Tower. Yes

The function will output all the key phrases from the input string to the console. No

The function uses RecognizeEntities to identify entities in the text, which typically includes named entities like locations (e.g., Paris, Eiffel Tower). It then prints the text of each recognized entity. The input string " Our tour of Paris included a visit to the Eiffel Tower " contains the entities " Paris " and " Eiffel Tower, " which will be broken down into individual words like " Paris, " " Eiffel, " and " Tower. " However, common words like " our " and " included " are not typically recognized as entities, and the function does not output all key phrases, only the recognized entities.

You might want to manage the translation and localization for the language understanding content for your bot independently.

Translate command in the @microsoft/bf-lu library takes advantage of the Microsoft text translation API to automatically machine translate .lu files to one or more than 60+ languages supported by the Microsoft text translation cognitive service.

What is translated?

An .lu file and optionally translate Comments in the lu file

LU reference link texts

List of .lu files under a specific path.

Code Recap

static void GetKeyPhrases ( TextAnalyticsClient textAnalyticsClient, string text)

{

var response = textAnalyticsClient.ExtractKeyPhrases(text);

Console.WriteLine( " Key phrases: " );

foreach ( string keyphrase in response.Value)

{

Console.WriteLine( $ " \t {keyphrase} " );

}

}

Then called with:

GetKeyPhrases(textAnalyticsClient, " the cat sat on the mat " );

Statement Analysis

“The call will output key phrases from the input string to the console.”

Yes.

The method calls ExtractKeyPhrases , iterates over response.Value , and writes each key phrase to the console.

“The output will contain the following words: the, cat, sat, on, and mat.”

No.

The Text Analytics key phrase extraction returns meaningful phrases, not stop words or every word.

Likely result: " cat " , " mat " , maybe " sat " depending on language model. But not " the " , " on " .

“The output will contain the confidence level for key phrases.”

No.

The ExtractKeyPhrases method in Text Analytics API returns only key phrases as strings, not confidence scores.

Final Answer

The call will output key phrases from the input string to the console → Yes

The output will contain the following words: the, cat, sat, on, and mat → No

The output will contain the confidence level for key phrases → No

Microsoft References

Azure Text Analytics – Key Phrase Extraction

TextAnalyticsClient.ExtractKeyPhrases

The scenario:

You are building an in-vehicle application that uses Azure AI Speech for text-to-speech (TTS).

Requirement: Optimize the quality of synthesized voice output.

You must choose the most appropriate SSML (Speech Synthesis Markup Language) attribute.

Option A – style attribute of the mstts:express-as element

Used to apply expressive styles like " cheerful, " " empathetic, " or " angry. "

Helpful for emotion/tone, but not specifically about audio quality for clarity in motor vehicles.

Option B – level attribute of the emphasis element

Controls how strongly certain words are emphasized.

Improves meaning and focus, but does not optimize overall voice quality.

Option C – pitch attribute of the prosody element ✅

The < prosody > element allows adjustments to pitch, rate, and volume.

Adjusting pitch directly affects the naturalness and clarity of the synthesized speech.

Especially useful in noisy environments such as cars, because modifying pitch can make speech stand out better against background noise.

This directly improves perceived quality of voice output.

Option D – effect attribute of the voice element

Used for special voice effects (like whispering).

Not typically used for general clarity or quality improvements.

Correct Answer: C. the pitch attribute of the prosody element

Microsoft References

SSML support in Azure AI Speech

Prosody element in SSML