Free and Premium Microsoft AI-102 Dumps Questions Answers

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

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.

C: Analyze Receipt - Get Analyze Receipt Result.

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

Request URL:

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.

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.

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.

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.

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.

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.

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

This scenario is about improving the accuracy of QnA Maker responses when user phrasing is different but semantically the same.

Problem

Uploaded document contains: “What is your warranty period?”

Users ask: “How long is the warranty coverage?”

QnA Maker fails because it does not automatically equate these two phrasings.

Both must map to the same answer.

Correct Sequence of Actions

Add alternative phrasing to the question and answer (QnA) pair.

In QnA Maker, you can add synonyms or paraphrases for a question.

Example: Add “How long is the warranty coverage?” as an alternative phrasing to the existing QnA pair.

This ensures the system treats both phrasings as the same question.

Retrain the model.

After modifying the knowledge base with new alternatives, you must retrain the model.

This step incorporates the updates into the QnA Maker service.

Republish the model.

To make the changes live and accessible by the chatbot endpoint, the retrained model must be republished.

Without republishing, the chatbot will still use the old knowledge base.

Actions That Are Not Required

Add a new QnA pair → Not needed, because we want both questions to map to the same existing answer, not duplicate answers.

Add additional questions to the document → That’s not required when QnA Maker already supports adding alternative phrasing within the portal or JSON editor.

Final Answer (Sequence)

Add alternative phrasing to the question and answer (QnA) pair.

Retrain the model.

Republish the model.

Microsoft References

QnA Maker – Add alternative phrasing

Train and publish a QnA Maker knowledge base

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

For a phone-style bot that listens to customers and replies with spoken output, you use two core Azure AI Speech SDK objects:

SpeechRecognizer performs speech-to-text (STT), turning the caller’s audio into text that your app can use to query the orders database. This minimizes effort because it handles audio capture/streaming, language models, and returns recognized text events/results directly.

SpeechSynthesizer performs text-to-speech (TTS), converting the retrieved order status text into natural-sounding audio to play back to the customer. It supports neural voices and simple calls like SpeakTextAsync() .

Other options are not appropriate here:

TranslationRecognizer is for real-time speech translation, not needed for simple STT.

VoiceProfileClient is for speaker verification/identification, not for recognition or synthesis of the conversation content.

Microsoft References

Azure AI Speech SDK – Speech to text (SpeechRecognizer) overview and quickstarts.

Azure AI Speech SDK – Text to speech (SpeechSynthesizer) overview and quickstarts.

Azure AI Speech SDK concepts – recognition vs. synthesis pipelines.

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).

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

Box 1: findsimilars

Box 2: matchPerson

Find similar has two working modes, " matchPerson " and " matchFace " . " matchPerson " is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person ' s other photos. Note that an empty list will be returned if no faces pass the internal thresholds. " matchFace " mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.

To quickly prototype harmful-content detection (e.g., discriminatory language) with minimal development, you should provision an Azure AI Content Safety resource in the Azure portal and then try it in Content Safety Studio using “Moderate text content.” Content Safety Studio lets you test text moderation, tune thresholds, and export sample code without writing much code up front. Creating an Azure OpenAI resource or using “Protected material detection for text” isn’t required for abusive/hate moderation; “protected material” targets copyrighted/credential leakage scenarios, not general toxicity.

Microsoft References:

Quickstart: Analyze text content (use Moderate text content in the studio; prerequisite is to create a Content Safety resource). Microsoft Learn

Azure AI Content Safety overview

Detect the incoming language:

Service: Speech to Text

Reasoning: The Speech to Text service can transcribe spoken language into text and includes language identification capabilities, allowing the system to detect whether the caller is speaking French or English.

Respond in the callers ' own language:

Service: Text to Speech

Reasoning: The Text to Speech service can convert text responses into spoken language in the detected language (French or English), enabling the system to respond to callers in their own language.

Ordered Mapping:

Detect the incoming language: Speech to Text

Respond in the callers ' own language: Text to Speech

Box 1: Text Analytics

The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.

Box 2: Translator

Translator is a cloud-based neural machine translation service that is part of the Azure Cognitive Services family of REST APIs. Translator can be used with any operating system and powers many Microsoft products and services used by thousands of businesses worldwide to perform language translation and other language-related operations.

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.

Correct Responses:

Yes

No

No

Microsoft References

Azure SQL Database – PaaS overview

What is PaaS?

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

To detect aggressive language (e.g., profanity/abuse) and sexually explicit content in text using the Content Moderator Text – Screen API:

classify = true

Enables machine-assisted text classification that returns category scores (e.g., sexually explicit/suggestive categories). This is required to identify sexual content.

language = < code > (for example, eng )

Specifies the language so profanity/abusive terms are detected correctly. The profanity detection in Text-Screen is language dependent.

autocorrect = true

Helps normalize intentionally misspelled or obfuscated words so that profanity or aggressive terms are still detected (e.g., “f@#$” → “fuck”).

Other parameters in the blade are not necessary for this goal:

PII targets personal data extraction, not aggression/sexuality.

listId is for matching against custom blocklists.

Headers like Ocp-Apim-Subscription-Key and Content-Type are required to call the API, but they are not the content-analysis settings that control detection behavior asked for in this question.

Microsoft Azure AI References

Azure AI Content Moderator – Screen text API parameters (autocorrect, language, classify) and category outputs.

Azure AI Content Moderator – Text moderation concepts: profanity detection and classification categories.

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

You are asked to build an app that translates speech from English into French, German, and Spanish using Azure AI Speech Translation.

Step 1 – Define target languages

The SpeechTranslationConfig object requires language codes (not full names like " French " , " German " , " Spanish " ).

Valid language codes:

French → " fr "

German → " de "

Spanish → " es "

Therefore, the correct syntax for the output languages is:

var languages = new List < string > { " fr " , " de " , " es " };

Step 2 – Choose the recognizer type

IntentRecognizer : Used for intent recognition with LUIS, not translation.

SpeakerRecognizer : Used for speaker identification/verification, not translation.

SpeechSynthesizer : Used for text-to-speech, not translation.

TranslationRecognizer : Used specifically for speech-to-speech or speech-to-text translation.

Therefore, the correct recognizer is TranslationRecognizer.

Correct Answer:

Output Languages: { " fr " , " de " , " es " }

Recognizer: TranslationRecognizer

Microsoft References

Speech Translation with Azure AI Speech

TranslationRecognizer Class (C# API)

Supported language codes

Scenario Recap:

Application: Flower species classification using Custom Vision .

New images are received.

Solution: Add the new images, and then use the Smart Labeler tool.

Analysis:

The Smart Labeler tool in Custom Vision can help automatically generate suggested labels for newly uploaded images.

This is a valid way to extend a classifier with new images.

It doesn’t complete the entire pipeline (you’d still retrain afterward), but using Smart Labeler does meet the goal of adding new species efficiently.

Correct Answer: A. Yes

Key Knowledge:

To analyze images with Azure OpenAI, you must use a GPT-4 model with vision support (model version vision-preview ).

This requires deploying a GPT-4 model for vision.

Then you use the Chat playground (not completions, not DALL-E) since you want analysis, not generation.

You need to give the assistant instructions in the system message field (e.g., “You are an AI assistant that describes images”).

Finally, you upload an image and prompt the model to describe it.

Correct Sequence:

Create a new deployment, select a GPT-4 model, and set Model version to vision-preview.

Because GPT-4 with vision is required.

Open Chat playground and select the deployed model.

Vision capabilities are accessed through the Chat playground.

In the System message field, enter: “You are an AI assistant that describes images.”

This sets the assistant’s role.

In the Chat session pane, enter a text prompt of “Describe this image,” and upload an image by using the attachment button.

This triggers the model to analyze and describe the image.

Verified Answer (Correct Order):

Create a new deployment, select a GPT-4 model, and set Model version to vision-preview.

Open Chat playground and select the deployed model.

In the System message field, enter: You are an AI assistant that describes images.

In the Chat session pane, enter: Describe this image , and upload an image by using the attachment button.

Microsoft References:

Azure OpenAI vision capabilities

Azure OpenAI Studio overview

Provision the Language service resource in Azure.

Deploy a Docker container to an on-premises server.

Run the container and query the prediction endpoint.

According to the Microsoft documentation, the Language service is a cloud-based service that provides various natural language processing features, such as sentiment analysis, key phrase extraction, named entity recognition, etc. You can provision the Language service resource in Azure by following the steps in Create a Language resource. You will need to provide a name, a subscription, a resource group, a region, and a pricing tier for your resource. You will also get a key and an endpoint for your resource, which you will use to authenticate your requests to the Language service API.

According to the Microsoft documentation, you can also use the Language service as a container on your own premises or in another cloud. This option gives you more control over your data and network, and allows you to use the Language service without an internet connection. You can deploy a Docker container to an on-premises server by following the steps in Deploy Language containers. You will need to have Docker installed on your server, pull the container image from the Microsoft Container Registry, and run the container with the appropriate parameters. You will also need to activate your container with your key and endpoint from your Azure resource.

According to the Microsoft documentation, once you have deployed and activated your container, you can run it and query the prediction endpoint to get sentiment analysis results. The prediction endpoint is a local URL that follows this < container IP address > : < port > /text/analytics/v3.1-preview.4/sentiment. You can send HTTP POST requests to this endpoint with your text input in JSON format, and receive JSON responses with sentiment labels and scores for each document and sentence in your input.

The agent will reason over the uploaded file.●○

The code will create a run and check the output.○●

The code will create an agent and enable a file search.○●

 The agent will reason over the uploaded file.

Yes : The code includes file = project_client.agents.upload_file_and_poll(file_path= " ./data/file1.zip " , purpose=FilePurpose.AGENTS). The upload_file_and_poll method with FilePurpose.AGENTS suggests that the file (file1.zip) is uploaded for the agent to process, and agents in the Azure AI Foundry Agent Service are typically designed to reason over uploaded data. This indicates the agent will reason over the uploaded file.

 The code will create a run and check the output.

No : The provided code snippet does not include any explicit call to create a run (e.g., a method like create_run or start_run) or check the output (e.g., a method to retrieve or poll run results). The code only uploads a file and creates a vector store, but it does not initiate or monitor a run.

 The code will create an agent and enable a file search.

No : The code does not contain a method to create an agent (e.g., create_agent) or explicitly enable a file search. The create_vector_store_and_poll method creates a vector store, which is typically used for indexing data for search, but there is no indication of enabling a file search or creating an agent in this snippet.

Box 1: mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment

To run the Sentiment Analysis v3 container, execute the following docker run command.

docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \

mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \

Eula=accept \

Billing={ENDPOINT_URI} \

ApiKey={API_KEY} is the endpoint for accessing the Text Analytics API. < your-custom-subdomain > .cognitiveservices.azure.com

Box 2:

{ENDPOINT_URI} is the endpoint for accessing the Text Analytics API: < your-custom-subdomain > .cognitiveservices.a The endpoint for accessing the Text Analytics API. zure.com

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

Explanation

To evaluate a Conversational Language Understanding (CLU) model, you can:

Use the REST authoring API to retrieve the model’s evaluation summary (precision, recall, F1 score).

In Language Studio, use the Model performance section to view evaluation metrics and confusion matrix.

Active Learning (option B) is for improving the model post-deployment by validating unclear utterances, not pre-deployment evaluation.

Log collection in Log Analytics (option D) is for monitoring, not formal evaluation.

Correct Answer: A and C

1. api-nam.cognitive.microsofttranslator.com

2. translate

Requests to Translator are, in most cases, handled by the datacenter that is closest to where the request originated. If there ' s a datacenter failure when using the global endpoint, the request may be routed outside of the geography.

To force the request to be handled within a specific geography, use the desired geographical endpoint. All requests are processed among the datacenters within the geography.

- United States

api-nam.cognitive.microsofttranslator.com

- translate

Translate specified source language text into the target language text.

Relational databases use SQL (Structured Query Language) to query and manipulate data.

B is incorrect: Relationships exist between tables (foreign keys, joins).

C is incorrect: Flexible schema is a characteristic of NoSQL, not relational.

D is incorrect: Relational databases aim to reduce duplicate data through normalization.

Comprehensive Detailed Explanation

When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:

Document cracking

The indexer opens the item (file/row) and extracts text, images, and metadata, creating the root of the enriched document (/document). This is always the first stage.

Field mappings

Next, source fields are mapped directly to target index fields before any transformations . Field mappings occur after document cracking but before skillset execution . Microsoft Learn

Skillset execution

Built-in or custom skills (for example, language and sentiment analysis in your scenario) run and write outputs into the in-memory enriched document. Microsoft Learn

Output field mappings

Because skill outputs live only in memory, you must define output field mappings to route selected nodes of the enriched document into index fields. This step comes after skillset execution.

Push to index

Finally, the indexer hands off raw and enriched content to the search index (the physical data structures) so it becomes searchable—i.e., the content is “pushed to the index.” Microsoft Learn

This order— document cracking → field mappings → skillset execution → output field mappings → push to index —ensures raw content is extracted, direct source-to-index mappings are applied, AI enrichment is performed, enriched outputs are projected to fields, and the completed documents are written into the index.

Microsoft Azure AI References

Indexers in Azure AI Search – Stages of indexing (Document cracking; Field mappings; Skillset execution; Output field mappings, with field mappings occurring before transformations). Microsoft Learn

Skillset concepts (Output of skillset is routed to the index via output field mappings after skillset execution). Microsoft Learn

AI enrichment concepts (Field mappings move raw source content to the index; output field mappings move enriched content; indexing ingests raw and enriched content into the physical index). Microsoft Learn

Annotations & enriched document tree (Enriched document is created during document cracking at /document). Microsoft Learn

The exhibit shows incremental increases by quarter leading to a cumulative running total , which is best represented with a waterfall chart .

Ribbon chart shows ranking changes over time, not cumulative totals.

Bar chart shows simple comparisons, not running totals.

Decomposition tree is for drill-down analysis, not running totals.

Correct Answer: A

Comprehensive Detailed Explanation

If a chatbot must query a knowledge base (KB), the Bot Framework uses QnAMakerDialog (inherited into Azure Cognitive Service for Language’s Question Answering).

AdaptiveDialog manages complex dialog flows but not specifically KB querying.

ComponentDialog organizes dialogs, but again, not specific for knowledge bases.

SkillDialog is used when invoking another bot (skill), not for KB queries.

Correct Answer: B. QnAMakerDialog

The task is to process incoming emails and route them to French or English support teams. This means we need to detect the language of each email.

Let’s break it down:

Which Cognitive Service is needed?

Azure Text Analytics (part of Azure AI Language) provides a Language Detection API.

The Translator service is used for translation, not just detecting the original language. Since the requirement is only to decide whether to send emails to French or English teams, we just need to detect the language.

Endpoint selection

Text Analytics uses regional endpoints such as:

< region > .api.cognitive.microsoft.com/

For example: eastus.api.cognitive.microsoft.com is valid.

api.cognitivemicrosofttranslator.com → incorrect (this is for Translator service).

portal.azure.com → Azure portal, not an API endpoint.

API Path selection

To detect language, you call:

/text/analytics/v3.1/languages

/text/analytics/v3.1/entities/recognition/general → entity recognition, not language.

/translator/text/v3.0/... → translation, not language detection.

Correct Answer Combination:

eastus.api.cognitive.microsoft.com

/text/analytics/v3.1/languages

Microsoft References

Text Analytics Language Detection API

Text Analytics REST API v3.1

A data warehouse is designed for analytical workloads (OLAP) , not transactional workloads (OLTP).

It integrates data from multiple sources and supports complex queries, aggregations, and reporting for decision-making.

A. storage for transactional LOB apps → describes OLTP (e.g., SQL Server, Azure SQL Database), not a data warehouse.

B. transformation services → describes ETL/ELT pipelines (e.g., Data Factory), not the warehouse itself.

C. read-only storage of historical data → partially true but too narrow; the key feature is analytics, not just storage.

D. answers to complex queries across multiple sources → Correct and complete definition.

Correct Answer: D

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.

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

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.

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.

SDK usage (analyze image from a stream): Microsoft Docs, Analyze an image by using the Computer Vision client library .

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

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.