Microsoft AI-102 Designing and Implementing a Microsoft Azure AI Solution Exam Practice Test

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

https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/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.

Comprehensive Detailed Explanation

The requirement is to build an Adaptive Card for the chatbot that supports multilingual product display and meets accessibility requirements.

Product Name Display

The product JSON stores localized names like " name " : { " en " : " ... " , " es " : " ... " , " pt " : " ... " } .

To dynamically render based on the current language, you must use name[language].

This ensures the product name shown in the card adapts automatically to the user’s preferred language.

Stock Level Warning

Business rules require warnings when stock is low or out of stock.

This means you display the stock warning only when stockLevel is not ' OK ' .

Therefore, the correct condition is:

" $when " : " ${stockLevel != ' OK ' } "

Image Alt Text for Accessibility

Accessibility requirements specify that all images must have alt text in English, Spanish, and Portuguese.

In the product JSON, " alttext " is also localized:

" altText " : { " en " : " Bicycle " , " es " : " Bicicleta " , " pt " : " Bicicleta " }

To render the correct localized alt text dynamically, use image.altText[language].

Correct Selections:

First blank: name[language]

Second blank: " $when " : " ${stockLevel != ' OK ' } "

Third blank: image.altText[language]

Microsoft References

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards

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.

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-vision/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

Scenario: All videos must have transcripts that are associated to the video and included in product descriptions.

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

Step 1: Upload the video to blob storage

Given a video or audio file, the file is first dropped into a Blob Storage. T

Step 2: Index the video by using the Video Indexer API.

When a video is indexed, Video Indexer produces the JSON content that contains details of the specified video insights. The insights include: transcripts, OCRs, faces, topics, blocks, etc.

Step 3: Extract the transcript from the Video Indexer API.

Step 4: Translate the transcript by using the Translator API.

Box 1: QnA Maker Editor

Scenario: Provide all employees with the ability to edit Q & As.

The QnA Maker Editor (read/write) has the following permissions:

Create KB API

Update KB API

Replace KB API

Replace Alterations

" Train API " [in

new service model v5]

Box 2: Contributor

Scenario: Only senior managers must be able to publish updates.

Contributor permission: All except ability to add new members to roles

You are deploying a Language Understanding (LUIS) container. When running LUIS in Docker, the model must first be exported from the Language Understanding portal, and then provided to the container at runtime.

Step-by-step reasoning:

From the Language Understanding portal, export the solution as a package file.

The trained LUIS model must be exported from the portal into a .json package file.

This is required because the container cannot access the hosted service directly.

From the host computer, move the package file to the Docker input directory.

Containers expect the model to be available locally.

The package file is placed into the input directory that the container maps for models.

From the host computer, run the container and specify the input directory.

When starting the container, you specify --volume < local_input > :/input so the container has access to the package file.

This makes the exported model available inside the container for processing.

Why not the other options?

Retain the model in the portal is not sufficient; the container cannot pull directly from the cloud.

Build the container and specify the output directory is not required; the container image is already available from MCR and is not custom-built for this step.

Correct Answer Order:

Export the solution as a package file.

Move the package file to the Docker input directory.

Run the container and specify the input directory.

Microsoft References

Run LUIS containers

Use containers with Azure AI services

The Text Moderation API of Azure Content Moderator is specifically used for scanning text to detect potentially inappropriate or undesired content.

Key features include:

Adult classification score → Indicates the likelihood that the text contains adult content.

Racy classification score → Indicates the likelihood that the text contains sexually suggestive but not explicit content.

PII detection (personal data) is part of text moderation but is not always required when focusing strictly on enabling content moderation for chatbot text safety.

OCR is for extracting text from images, not for moderating chatbot conversations.

Text classification in this context refers to general machine learning categorization (not part of Content Moderator’s moderation API).

Thus, the two service responses directly tied to text moderation safety are:

Adult classification score

Racy classification score

Correct Answer for Q185: A and E

Microsoft Reference

Azure Content Moderator text moderation overview

In the Azure portal, the Keys and Endpoint blade for a Cognitive Service resource provides:

API keys (for authentication)

Endpoint URL (the REST interface base URL)

Identity blade relates to managed identities.

Properties blade shows metadata (resource ID, location, subscription ID).

Networking blade controls network access and firewall rules.

Correct Answer: B. Keys and Endpoint

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

Comprehensive Detailed Explanation along with All References available from Microsoft Azure AI Solution at the end of the explanation

Typing: In a .chat transcript, you can simulate the bot’s typing indicator with the Typing activity, optionally followed by a [Delay=...] . This matches the intent to pause before sending the next message.

AttachmentLayout=carousel: When sending multiple attachments (the two watch images), set the layout to carousel so the Emulator displays the attachments horizontally as a card carousel.

Adaptive Card attachment: The JSON file cards\watchProfileCard.json represents an Adaptive Card; specify adaptivecard so the Emulator renders the card.

Resulting .chat snippet (essentials):

user =User1

bot=watchbot

user : I want a new watch.

bot: [Typing][Delay= 3000 ]

bot: I can help you with that! Let me see what I can find.

bot: Her e ' s what I found.

bot:

[AttachmentLayout=carousel]

[Attachment=https://contoso.blob.core.windows.net/watch01.jpg]

[Attachment=https://contoso.blob.core.windows.net/watch02.jpg]

user: I like the first one.

bot: Sure, pulling up more information.

bot: [Attachment=cards\watchProfileCard.json adaptivecard]

user: That ' s nice! Thank you.

bot: Sure, you are most welcome!

References

Bot Framework Emulator transcript ( .chat ) activities and typing indicator: Bot Framework Activity schema; Emulator supports typing and delay in transcripts.

https://learn.microsoft.com/azure/bot-service/bot-service-debug-emulator

https://learn.microsoft.com/azure/bot-service/rest-api/bot-framework-rest-connector-api-reference#activity-object

Attachment layout ( carousel / list ) for multiple attachments:

https://learn.microsoft.com/azure/bot-service/nodejs/bot-builder-nodejs-send-rich-cards#carousel-layout

Adaptive Cards in Bot Framework:

https://learn.microsoft.com/adaptive-cards/get-started/bots

https://learn.microsoft.com/azure/bot-service/bot-builder-howto-add-media-attachments#adaptive-cards

Comprehensive Detailed Explanation

The scenario is identical to Question 43:

You have a FindContact intent.

Training phrases include city names (London, Seattle, Ukraine).

You must model location properly in Language Understanding.

Why the Proposed Solution is Wrong

The solution says: “Create a new entity for the domain.”

Entities in LUIS/CLU represent specific data types to extract (e.g., location, date, contact name).

Domains in Language Understanding are prebuilt sets of intents and entities for common use cases (like Calendar, Home Automation, Communication).

Creating an “entity for the domain” is conceptually incorrect — entities are not meant to represent domains.

What we really need is to model location as an entity (for example, using the prebuilt GeographyV2 entity), not to create a generic “domain entity.”

Correct Approach

Keep the FindContact intent.

Add a Location entity (prebuilt GeographyV2 or custom “Location”).

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

Option Analysis

A. Yes → Incorrect. This would not meet the goal.

B. No → Correct. Creating a domain entity is not the right way; instead, a proper location entity should be added.

Correct Answer: B. No

Microsoft References

Intents vs Entities in Language Understanding

Prebuilt entity: GeographyV2

LUIS domains overview

In the Bot Framework SDK, if you want to collect structured input step by step, you typically use a Waterfall dialog. Each step in the waterfall represents one piece of logic, such as asking for the size, toppings, or type of food item.

Prompts are used inside waterfall steps to actually ask the user for input (e.g., text prompt, choice prompt, date-time prompt).

Adaptive, Action, Input are concepts more relevant to Power Virtual Agents or Adaptive Dialogs (Composer), not the Bot Framework SDK question context.

Since the question specifically mentions Bot Framework SDK, the correct answer is Waterfall dialog + Prompts.

Correct Answer: C, D

Normalization involves eliminating relationships between database tables.

No

Normalization does not eliminate relationships. In fact, it often creates relationships between tables by splitting data into smaller, related tables.

Normalizing a database reduces data redundancy.

Yes

The primary goal of normalization is to minimize duplicate data and ensure that each fact is stored only once.

Normalization improves data integrity.

Yes

By reducing redundancy and ensuring data is stored in a structured manner, normalization enforces consistency and integrity across the database.

Correct Responses:

No

Yes

Yes

Microsoft References

Database normalization basics

Database design and normalization

You have a chatbot built on Azure OpenAI (AI1). The requirement is that all input and output from the chatbot must be checked for objectionable content (such as hate speech, sexual, violent, or self-harm content).

To meet this requirement, Microsoft recommends integrating Azure AI Content Safety with Azure OpenAI.

Azure AI Content Safety provides APIs to detect and filter harmful or objectionable text and images.

For chatbot scenarios, you can configure Content Safety as a moderation layer on both user input (prompt) and model output (response).

This ensures safe interactions before messages are returned to users.

Other options:

A. Azure Machine Learning – used for building, training, and deploying custom ML models, not for content moderation.

B. Log Analytics – used for monitoring and logging, not for filtering objectionable content.

D. Microsoft Defender Threat Intelligence (Defender TI) – provides cybersecurity intelligence, not responsible for moderating chatbot content.

Therefore, the correct resource to create is Azure AI Content Safety.

Correct Answer: C. Azure AI Content Safety

Microsoft References

Azure OpenAI and Azure AI Content Safety integration

Azure AI Content Safety overview

Moderate text with Azure AI Content Safety

Creating an entity for price is a feature of Conversational Language Understanding (CLU/LUIS), not Question Answering. Custom question answering does not use entities for extraction or intent resolution; it matches user utterances to Q/A pairs using alternates, synonyms, and ranking. Therefore, defining an “entity for price” would not make the chatbot correctly answer “How much does < product > cost?”. The correct fix is to author additional alternate phrasings (as in Q266).

References

Custom question answering capabilities (no entity modeling).

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/overview

CLU entities vs. QnA authoring—separate features and workflows.

https://learn.microsoft.com/azure/ai-services/language-service/conversational-language-understanding/overview

You need to detect PPE compliance (masks or safety glasses) on a periodic schedule (every 15 minutes) while minimizing development effort and cost.

Why Computer Vision (Azure AI Vision)

Azure AI Vision (formerly “Computer Vision”) lets you analyze still images with prebuilt Image Analysis and—when needed—train a lightweight custom object detection model directly in Vision Studio without building ML pipelines. You can simply grab a frame (snapshot) from your camera feed every 15 minutes and call the Vision API—this keeps costs low because you pay per image analyzed rather than continuously processing video. Microsoft’s current Vision stack supports image analysis and custom object detection via Vision Studio and v4.0 APIs, designed for scenarios like PPE detection. Microsoft Azure +1

Why not Face

The Face service is intended for face detection/recognition workflows. While Face can return some accessory attributes (e.g., glasses/mask confidence), Microsoft has scaled back several face attributes under Responsible AI guidance, and Face is not positioned as a general PPE-compliance detector across a scene (multiple people, full body PPE). It’s therefore a poorer fit and can increase false decisions for PPE checks beyond the face region. Azure Docs +3

Why not Azure Video Analyzer for Media (Video Indexer)

Video Indexer is optimized for extracting insights from full videos (speech, people, labels, brands). Running it just to check PPE every 15 minutes is overkill and more expensive than analyzing a single still frame with Vision. In addition, Microsoft has been adjusting video services post–Azure Media Services retirement; using snapshot + Vision avoids those complexities and keeps costs minimal.

How this meets the requirements

Identify masks/safety glasses: Use Image Analysis or a quick custom object detector in Vision Studio trained on a small set of PPE images. Microsoft Learn

Every 15 minutes: Capture a single frame from the camera and call the Vision endpoint—simple scheduling with a timer-triggered Function or Logic App, minimal code. (General Azure pattern; Vision API is stateless per image.) Microsoft Azure

Minimize development effort: Vision Studio provides no-code/low-code training and testing for custom detectors if the prebuilt model isn’t sufficient. Microsoft Learn

Minimize costs: Pay-per-call image analysis is cheaper than indexing entire video segments; no continuous streaming required. Microsoft Azure

Answer: B. Computer Vision

Microsoft References

Azure AI Vision (product overview): image and video analysis with OCR, object detection, Image Analysis. Microsoft Azure

What’s new in Azure AI Vision: custom image classification/object detection in Vision Studio and v4.0 APIs. Microsoft Learn

Face detection & attributes (accessories including glasses and mask)—not the right tool for scene-level PPE compliance. Microsoft Learn

Face Detect REST API (attributes list, including glasses/mask). Microsoft Learn

Responsible AI updates indicating changes to facial analysis attributes; facial detection/accessories remain but are limited in scope. Azure Docs

Azure AI Video Indexer release notes/updates (video-centric use cases, ongoing adjustments post–AMS retirement).

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

Comprehensive Detailed Explanation

Let’s examine the function:

static void GetKeyWords ( TextAnalyticsClient textAnalyticsClient, string text)

{

var response = textAnalyticsClient.RecognizeEntities(text);

Console.WriteLine( " Key words: " );

foreach (CategorizedEntity entity in response.Value)

{

Console.WriteLine( $ " {entity.Text} " );

}

}

The function uses RecognizeEntities from the Azure Text Analytics SDK.

RecognizeEntities detects and categorizes named entities in text (such as locations, people, organizations, dates, etc.).

It does not extract all words or key phrases.

For extracting key phrases, you would use ExtractKeyPhrases , not RecognizeEntities .

Evaluating the statements:

" The output will include the following words: our and included. "

These are common words, not entities. They will not appear.

Answer: No

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

These are named entities (location, landmark). They will be recognized and returned.

Answer: Yes

" The function will output all the key phrases from the input string to the console. "

No. The function extracts entities, not key phrases. Key phrases would require ExtractKeyPhrases .

Answer: No

The HTTP request provided is:

POST https://management.azure.com/subscriptions/18c51a87-3a69-47a8-aedc-a54745f708a1/resourceGroups/RG1/providers/Microsoft.CognitiveServices/accounts/contosol/regenerateKey?api-version=2017-04-18

Body { " keyName " : " Key2 " }

Key Observations:

The request is made against the Azure Cognitive Services Management API ( Microsoft.CognitiveServices/accounts/.../regenerateKey ).

The regenerateKey operation is specifically designed to regenerate one of the two subscription keys used to authenticate Cognitive Services API calls.

Cognitive Services resources always have two keys: Key1 (primary) and Key2 (secondary).

This design allows key rotation without downtime: you can regenerate one key while using the other in production.

Body Payload:

{ " keyName " : " Key2 " }

This explicitly tells Azure to regenerate Key2 (the secondary subscription key).

After the call, the secondary subscription key value changes, while Key1 (primary) remains unaffected.

Option Analysis

A. A key for Azure Cognitive Services was generated in Azure Key Vault

Incorrect. The operation is not integrated with Key Vault; it only regenerates Cognitive Services subscription keys.

B. A new query key was generated

Incorrect. Query keys are related to Azure Cognitive Search (not general Cognitive Services).

The request clearly targets CognitiveServices/accounts.

C. The primary subscription key and the secondary subscription key were rotated

Incorrect. The request regenerates only the specified key, not both.

D. The secondary subscription key was reset

Correct. The payload specifies " Key2 " , so only the secondary subscription key is regenerated.

Correct Answer: D. The secondary subscription key was reset

Microsoft References

Azure REST API – Cognitive Services regenerate key

Authenticate requests to Azure AI services with keys and endpoint

Manage Cognitive Services keys (key1/key2)

A. True

B. True

C. True

B: see this example code from documentation that uses PersonGroup of size 10,000 :

https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/how-to-add-faces

the question wants to trick you into thinking you need to use a LargePersonGroup for a size of 10,000 - but the documentation for it doesn ' t include this limitation or criteria: https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/how-to-use-large-scale

“Monitor online activity” in Content Safety Studio shows usage and moderation results and allows viewing category distributions; it’s meant for observability of live traffic , not for running offline tests on sample questions to tune filter settings. Hence, it doesn’t meet the requirement. Content Safety Studio

References

Azure AI Content Safety – Protected material detection concepts & quickstart.

Azure OpenAI – System message (metaprompt) guidance. Microsoft Learn

Content Safety Studio – Monitor online activity page. Content Safety Studio

Azure OpenAI content filtering system and configuration overview.

Code Recap (Custom Skill JSON Schema)

{

" @odata.type " : " #Microsoft.Skills.Custom.WebApiSkill " ,

" description " : " My custom skill description " ,

" uri " : " https://contoso-webskill.azurewebsites.net/api/process " ,

" context " : " /document/organizations/* " ,

" inputs " : [

{

" name " : " companyName " ,

" source " : " /document/organizations/* "

}

],

" outputs " : [

{

" name " : " companyDescription "

}

]

}

Statement Analysis

CompanyDescription is available for indexing.

Yes.

The skill produces an output field called companyDescription . Outputs of a custom skill become part of the enrichment tree, which can then be projected into the search index or knowledge store.

The definition calls a web API as part of the enrichment process.

Yes.

This is a Web API skill ( @odata.type: #Microsoft.Skills.Custom.WebApiSkill ) with a uri pointing to https://contoso-webskill.azurewebsites.net/api/process . This means an external web API is called to enrich data.

The enrichment step is called only for the first organization under /document/organizations .

No.

The context is set to " /document/organizations/* " , where the * means the skill runs for each element in the collection. It is not limited to the first element.

Final Answer

CompanyDescription is available for indexing → Yes

The definition calls a web API as part of the enrichment process → Yes

The enrichment step is called only for the first organization under /document/organizations → No

Microsoft References

Custom skills in Azure Cognitive Search

Enrichment tree and skill context

To let an agent use public web data with recency controls (e.g., limit to content from the last 90 days), you add the Bing grounding tool. In the Azure AI Foundry Agent Service SDK, you create a connection (e.g., search_connection_id ) and then construct a BingGroundingTool with that connection. The tool exposes a list of definitions (tool operations) that the agent can call at runtime.

When creating the agent, you pass tool definitions via the tools= argument (a list of ToolDefinition ). The tool_resources= argument is used when the tool requires additional resource descriptors (e.g., Azure AI Search index configuration). For Bing grounding, providing the connection and passing the definitions as tools= is sufficient. This enables the agent to call Bing with date filters to retrieve only recent, publicly accessible content.

Microsoft Azure AI References (titles only)

Azure AI Agent Service – Bing web grounding tool

Azure AI Agent Service – Create an agent (agent creation parameters including tools and tool_resources )

Azure AI Agent Service – Tool connections and definitions

Upload a glossary file to the container for French files

Define a document translation specification that has a French target

Perform an asynchronous translation by using the document translation specification

You need to translate Word and PowerPoint files (German → French) while preserving formatting and supporting a custom glossary. The correct service is Azure Document Translation, part of the Translator service in Azure Cognitive Services.

Step-by-step reasoning:

Upload a glossary file to the container for French files.

Glossaries (also called custom terminology files) guide translation to ensure consistent word usage.

They are stored in the target container (in this case, the French container).

Define a document translation specification that has a French target.

A translation request is defined by creating a document translation specification JSON.

This specification indicates:

The source container (German files)

The target container (French files)

The glossary file (for custom terminology)

Output target language (French).

Perform an asynchronous translation by using the document translation specification.

Document translation jobs are submitted asynchronously.

This step executes the translation while preserving formatting and applying the glossary rules.

Why not the other options?

Generate a list of files to be translated → not required, the service automatically processes all files in the source container.

Perform asynchronous translation using a list of files → that method does not support document-level translation with glossary and formatting preservation, it’s used for text-based translations.

Correct Answer Order:

Upload a glossary file to the container for French files

Define a document translation specification that has a French target

Perform an asynchronous translation by using the document translation specification

Microsoft References

Document Translation – Translate documents in containers

Use custom glossaries with Document Translation

Submit document translation requests

Box 1: No

Example: Greet a user whose name is stored in `user.name`

- ${ welcomeUser(user.name) }

Example: Greet a user whose name you don ' t know:

- ${ welcomeUser() }

Box 2: No

Greet(User) is a Send a response action.

Box 3: Yes

To detect common data types such as temperatures, currency values, email addresses, and telephone numbers with minimal development effort, you should use pre-built entity components in Conversational Language Understanding (CLU). Prebuilt components are ready-made recognizers that automatically extract well-known patterns from utterances—no training, labeling, lists, or regex authoring required.

Specifically, CLU offers the following relevant prebuilt components:

Quantity.Temperature – extracts temperatures (e.g., “34 degrees celsius”).

Quantity.Currency – extracts monetary amounts and currency units (e.g., “$20”, “£15.50”).

Email – extracts email addresses.

Phone Number – extracts phone numbers.

These components are designed to be dropped into your entities and immediately provide predictions, which minimizes development effort compared to:

List entities (you must curate values/synonyms yourself),

Learned entities (require labeling utterances and training),

Regular expression components (you must craft and maintain regex patterns), or

Utterances (training data, not a capability for detection by itself).

Microsoft References

Supported prebuilt entity components in CLU (shows Quantity.Temperature, Quantity.Currency, Email, Phone Number, etc.). Microsoft Learn

Entity components in CLU (explains prebuilt, learned, list, and regex components and why prebuilt components are automatically detected). Microsoft Learn

We need to design a Conversational Language Understanding (CLU) model for smart home commands.

The sample utterances:

“Set all the lights to on.” → Intent = change setting, Entity = device (lights)

“Turn off the lights in the living room.” → Intent = change setting, Entities = device (lights) + location (living room)

“What is the current thermostat temperature?” → Intent = query setting, Entity = device (thermostat)

“Lower the temperature of the thermostat by five degrees.” → Intent = change setting, Entity = device (thermostat)

Breakdown:

Intents needed:

Change setting intent (D): Covers actions like turning devices on/off, lowering temperature.

Query setting intent (E): Covers questions about current state, like “What is the thermostat temperature?”

Entities needed:

Device entity (F): Identifies devices such as lights and thermostat .

Why not the others:

A. a location intent → Location (like “living room”) should be modeled as an entity, not an intent.

B. a change setting entity → Change setting is an action (intent), not an entity.

C. a device intent → Devices are entities, not intents.

Correct Answer:

D. a change setting intent

E. a query setting intent

F. a device entity

Microsoft References

Intents and entities in CLU

CLU model design best practices

To validate that your Azure AI Content Safety resource can identify images containing potential violence, you must call the Image Analyze operation of Content Safety. The REST endpoint for this operation is:

POST {endpoint}/contentsafety/image:analyze?api-version=2024-09-01

This operation analyzes an image for the four harm categories Hate, SelfHarm, Sexual, and Violence. Therefore, the correct base path is /contentsafety, and the correct operation is image analyze (shown in the hotspot as /imageanalyze).

Microsoft References

REST API: Analyze Image — POST {endpoint}/contentsafety/image:analyze?api-version=2024-09-01 ; categories include Hate, SelfHarm, Sexual, and Violence. Microsoft Learn

Quickstart: Analyze image content with Azure AI Content Safety (overview and examples). Microsoft Learn

Harm categories concept (lists Violence among supported categories). Microsoft Learn

The statement is:

" In an infrastructure as a service (IaaS) instance of Microsoft SQL Server on Azure, you manage the ______ that hosts SQL Server. "

Options:

Elastic pool → A PaaS concept in Azure SQL Database (used to share resources across multiple databases). Not IaaS.

MySQL server → Refers to Azure Database for MySQL (PaaS). Not related to SQL Server on IaaS.

PostgreSQL server → Refers to Azure Database for PostgreSQL (PaaS). Not related to SQL Server on IaaS.

Virtual machine → In IaaS, SQL Server is installed on an Azure VM. You manage the VM (OS, patches, SQL installation, backups, etc.). ✅

Thus, for IaaS, you are responsible for managing the virtual machine that hosts SQL Server.

Correct Answer: virtual machine

Microsoft References

SQL Server on Azure Virtual Machines overview

Compare Azure SQL Database (PaaS) vs SQL Server on VMs (IaaS)

The requirement:

You have a system monitoring temperature data from a single data stream.

The system must generate an alert in response to atypical values.

The solution must minimize development effort.

Analysis of Options:

Univariate Anomaly Detection

Designed for detecting anomalies in a single time series (e.g., temperature, pressure, humidity, sales).

Since the system monitors temperature only, this is a classic univariate case.

Provides simple integration with minimal effort.

Correct choice.

Azure Stream Analytics

Can process real-time streaming data and detect conditions (with custom queries).

However, it requires more development effort since you would need to manually code logic for anomaly detection.

Not the minimal-effort option.

Metric alerts in Azure Monitor

Used to trigger alerts on predefined thresholds (static rules).

Not intelligent anomaly detection—does not automatically learn patterns from data.

Would require manual threshold setup, which is not efficient for anomaly detection.

Multivariate Anomaly Detection

Used when analyzing multiple related time series (e.g., temperature, pressure, vibration all together).

Since this scenario only has one variable (temperature), multivariate is unnecessary and adds complexity.

Correct Answer: A. Univariate Anomaly Detection

Microsoft References

Azure AI Anomaly Detector – Univariate

Multivariate Anomaly Detector

Difference between univariate and multivariate anomaly detection

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.

C:\Users\Waqas Shahid\Desktop\Mudassir\Untitled.jpg

When deploying Cognitive Services containers (such as Anomaly Detector), the container requires a billing parameter in the docker run command.

This parameter points to the Azure resource key and endpoint for billing.

Fluentd is for logging.

Http Proxy is optional for outbound connectivity.

Mounts are used for local storage but are not mandatory for billing.

Correct Answer: B

Obtain values to connect your bot to the knowledge base

1.In the QnA Maker site, select your knowledge base.

2.With your knowledge base open, select the SETTINGS tab. Record the value shown for service name. This value is useful for finding your knowledge base of interest when using the QnA Maker portal interface. It ' s not used to connect your bot app to this knowledge base.

3.Scroll down to find Deployment details and record the following values from the Postman sample HTTP request:

4.POST /knowledgebases/ < knowledge-base-id > /generateAnswer

5.Host: < your-host-url >

6.Authorization: EndpointKey < your-endpoint-key >

Comprehensive Detailed Explanation

You have 1,000 untagged images and want to retrain the Custom Vision model as quickly as possible. The key is to avoid manually tagging every image, since that is time-consuming.

Step 1: Upload all the images

First, bulk upload all the untagged images into the Custom Vision project.

At this stage, they will have no labels.

Step 2: Get suggested tags

The Custom Vision portal offers an auto-suggest feature that uses the current trained model to predict tags for new, untagged images.

This dramatically reduces manual tagging effort.

Step 3: Review the suggestions and confirm the tags

Auto-suggestions are not always 100% accurate, so you must confirm or correct them.

Once confirmed, the dataset is labeled properly and ready for retraining.

Why not the other options?

Upload the images by category / group into category folders: This is only helpful if the images already have known categories, but here they arrive untagged.

Tag the images manually: This would take much longer and contradicts the requirement to minimize time.

Correct Sequence

Upload all the images.

Get suggested tags.

Review the suggestions and confirm the tags.

Microsoft References

Custom Vision – Tag images

Custom Vision – Suggested tags with predictions

Scenario: When the response confidence score is low, ensure that the chatbot can provide other response options to the customers.

When no good match is found by the ranker, the confidence score of 0.0 or " None " is returned and the default response is " No good match found in the KB " . You can override this default response in the bot or application code calling the endpoint. Alternately, you can also set the override response in Azure and this changes the default for all knowledge bases deployed in a particular QnA Maker service.

Choosing Ranker type: By default, QnA Maker searches through questions and answers. If you want to search through questions only, to generate an answer, use the RankerType=QuestionOnly in the POST body of the GenerateAnswer request.

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.

Step 1: Create a new project and load sample documents

Create a new project. Projects store your configurations and settings.

Step 2: Label the sample documents

When you create or open a project, the main tag editor window opens.

Step 3: Train a custom model.

Finally, train a custom model.

Box 1: " categories " : [ " Locations " , " Persons " , " Organizations " ],

Locations, Persons, Organizations are in the outputs.

Scenario: Contoso plans to develop a searchable knowledgebase of all the intellectual property

Note: The categories parameter is an array of categories that should be extracted. Possible category types: " Person " , " Location " , " Organization " , " Quantity " , " Datetime " , " URL " , " Email " . If no category is provided, all types are returned.

Box 2: { " name " : " entities " }

The include wikis, so should include entities in the outputs.

Note: entities is an array of complex types that contains rich information about the entities extracted from text, with the following fields

name (the actual entity name. This represents a " normalized " form)

wikipediaId

wikipediaLanguage

wikipediaUrl (a link to Wikipedia page for the entity)

etc.

Scenario: All planned projects must support English, French, and Portuguese.

If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases), you can use the Dispatch tool to determine which LUIS model or QnA Maker knowledge base best matches the user input. The dispatch tool does this by creating a single LUIS app to route user input to the correct model.

The requirements state:

All AI solution responses must have a confidence score ≥ 70%.

If the response confidence score is < 70%, the response must be improved by human input.

Members of the Consultant-Bookkeeper group must be able to process financial documents, which includes performing manual review when the AI confidence is below threshold.

Looking at the provided JSON:

" fields " : {

" ReceiptType " : {

" type " : " string " ,

" valueString " : " Itemized " ,

" confidence " : 0.672

},

" MerchantName " : {

" type " : " string " ,

" valueString " : " Tailwind " ,

" confidence " : 0.913

}

}

ReceiptType.confidence = 0.672 → this is below 0.7.

MerchantName.confidence = 0.913 → this is above 0.7.

Therefore, the correct condition for triggering manual review is:

documentResults.fields. < field > .confidence < 0.7

Now, evaluating the options:

A. documentResults.docType == " prebuilt:receipt "

Always true for receipts, does not check confidence. Not correct.

B. documentResults.fields.*.confidence < 0.7

This is the correct general expression: trigger manual review whenever any field confidence is below 0.7.

C. documentResults.fields.ReceiptType.confidence > 0.7

This would bypass manual review when ReceiptType has high confidence. The requirement is to trigger review when confidence < 0.7, so this is the opposite.

D. documentResults.fields.MerchantName.confidence < 0.7

This only checks one field (MerchantName). In the JSON, MerchantName has confidence 0.913 ( > 0.7), so this condition would not trigger, but ReceiptType clearly needs review. Too narrow.

Correct Answer: B. documentResults.fields.*.confidence < 0.7

Microsoft References

Azure AI Document Intelligence – Confidence scores

Human-in-the-loop for Document Intelligence

The wiki contains text in English, French and Portuguese.

Scenario: All planned projects must support English, French, and Portuguese.

The Document Extraction skill extracts content from a file within the enrichment pipeline. This allows you to take advantage of the document extraction step that normally happens before the skillset execution with files that may be generated by other skills.

Note: The Translator Text API will be used to determine the from language. The Language detection skill is not required.

C: Analyze Receipt - Get Analyze Receipt Result.

Query the status and retrieve the result of an Analyze Receipt operation.

Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}

E: POST {Endpoint}/vision/v3.2/read/analyze

Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.

Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents

The document processing solution must be able to process standardized financial documents that have the following characteristics:

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

*The document processing solution must be able to extract tables and text from the financial documents.

The document processing solution must be able to extract information from receipt images.

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

Can search content in different formats, including video

Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.

Keywords extraction: Extracts keywords from speech and visual text.

Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).

Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.

Artifacts: Extracts rich set of " next level of details " artifacts for each of the models.

Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.