AI-102 Designing and Implementing a Microsoft Azure AI Solution Question and Answers

Searchable → Field content is tokenized and added to the full-text index (supports free-text queries).

Retrievable → Field values can be included in search results.

Sortable → Used for ordering results, not required here.

Facetable → Used for aggregation/filtering by categories, not required here.

Filterable → Used for structured filters (e.g., WHERE conditions), not required here.

You’re indexing content with Azure AI Search and you want to recognize and retrieve properties from invoices (such as invoice number, vendor, dates, totals, line items). The Azure service designed for structured information extraction from forms and documents is Azure AI Document Intelligence (formerly Form Recognizer).

In an Azure AI Search enrichment pipeline, you add a skillset. To pull structured fields from invoices, include a skill that invokes Document Intelligence’s prebuilt Invoice model. This model already extracts common invoice fields, so you don’t have to train anything—meeting the goal of minimal effort. You can wire this in using the built-in Document Intelligence skill for Search, or as a custom Web API skill that calls the Document Intelligence REST API, and then map the extracted fields into your index for retrieval and filtering.

Why the others are not suitable:

Azure OpenAI is for generative/LLM tasks, not schema-level invoice field extraction.

Azure AI Immersive Reader improves reading accessibility; it doesn’t extract business fields.

Azure Custom Vision is for image classification/object detection, not form/key-value extraction.

Microsoft References (Azure AI Solution)

Azure AI Search skillsets – add AI enrichment and custom skills (Web API skill).

Document Intelligence skill for Azure AI Search (formerly Form Recognizer skill) – integrate extraction into indexing.

Prebuilt Invoice model – fields extracted (vendor, invoice ID/date, totals, taxes, line items, etc.).

Azure AI Document Intelligence overview – key-value extraction and table/line-item parsing from documents.

The question is about evaluating a Custom Vision classifier after training.

When you train an image classifier in Azure Custom Vision, the service automatically calculates performance metrics to help you evaluate the quality of the model.

Metrics Provided by Custom Vision:

Precision:

The percentage of correct positive predictions out of all positive predictions made.

Formula: Precision = TP / (TP + FP)

Helps determine how reliable positive predictions are.

Recall:

The percentage of actual positives that were correctly predicted.

Formula: Recall = TP / (TP + FN)

Helps determine how many of the true positives were captured.

These two metrics are explicitly shown in the Custom Vision portal and via API.

Option Analysis

A. Recall

Yes, Custom Vision reports recall.

Correct

B. F-score

Not directly reported in the Custom Vision portal.

Although it can be derived from precision and recall, it is not provided as a direct metric.

Incorrect

C. Weighted accuracy

Not reported by Custom Vision.

Incorrect

D. Precision

Yes, Custom Vision reports precision.

Correct

E. Area under the curve (AUC)

Not reported by Custom Vision.

More common in ROC curve analysis, not part of Custom Vision output.

Incorrect

Correct Answer: A. recall, D. precision

Microsoft References

Evaluate the prediction performance of your classifier in Custom Vision

Custom Vision training and evaluation

Event log data is typically semi-structured (key-value pairs, JSON-like format).

Azure Table Storage is a NoSQL key-value store that is ideal for storing semi-structured, non-relational datasets .

Logs arrive as events occur, so they can be appended as rows in table storage.

Other options:

Azure Queue storage → Used for messaging between application components, not for storing logs.

Azure Files → Cloud-based SMB file shares, not optimized for event log storage.

Correct Answer: Azure Table storage

To generate complete sentences describing an image (for accessibility for visually impaired users), you must call the Describe Image API.

describeImageInStreamAsync analyzes the content of the image and generates human-readable descriptions in natural language sentences.

readInStreamAsync is for OCR (extracting text).

tagImageInStreamAsync is for generating tags (keywords), not full sentences.

analyzeImageByDomainInStreamAsync is for domain-specific models (e.g., celebrities, landmarks).

Correct Answer: B

Azure AI Document Intelligence (formerly Form Recognizer) is provisioned as a Cognitive Services account with the kind set to FormRecognizer . In an ARM template, Document Intelligence is created under the resource provider Microsoft.CognitiveServices with the resource type accounts . Therefore:

type must be " Microsoft.CognitiveServices/accounts "

kind must be " FormRecognizer "

This aligns with Microsoft’s ARM schema for Cognitive Services accounts and the documented way to deploy Document Intelligence.

Key references from Microsoft documentation:

Azure Resource Manager template reference for Cognitive Services accounts — shows type: Microsoft.CognitiveServices/accounts and supported kind values including FormRecognizer .

https://learn.microsoft.com/azure/templates/microsoft.cognitiveservices/accounts

Azure AI Document Intelligence (Form Recognizer) resource creation guidance — indicates the service is deployed as a Cognitive Services account with kind FormRecognizer .

https://learn.microsoft.com/azure/ai-services/document-intelligence/overview

Create Document Intelligence resources (portal/ARM/Bicep) – reiterates Cognitive Services account with FormRecognizer kind.

https://learn.microsoft.com/azure/ai-services/document-intelligence/create-resources

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

The provided code uses the Azure AI Language – Text Analytics client to call the ExtractKeyPhrases method.

The method ExtractKeyPhrases(text) identifies the most important words and phrases in the input text.

The input string is:

" the quick brown fox jumps over the lazy dog "

The ExtractKeyPhrases API removes stopwords such as " the, " " over, " and " jumps " and extracts only meaningful keywords.

Therefore, the returned list of key phrases will include:

" Quick brown fox "

" Lazy dog "

When printed, the console output after " Key phrases: " will show:

Quick brown fox

Lazy dog

This matches option C (Quick brown fox lazy dog), since the key phrases are concatenated as output lines.

Correct Answer: C. Quick brown fox lazy dog

Microsoft References

Azure AI Language – Key phrase extraction

TextAnalyticsClient.ExtractKeyPhrases method (Azure.AI.TextAnalytics)

Comprehensive Detailed Explanation

The scenario:

You are working with Azure AI Video Indexer.

You need to add a custom brand/logo and also configure an exclusion so that this brand is not indexed.

In Video Indexer’s REST API for brands, the configuration JSON includes:

" enabled " : true/false → Determines whether the brand should be indexed.

Setting " enabled " : false means the brand is excluded.

The other properties in the dropdown ( state , tags , useBuiltin ) are not correct in this context for configuring exclusions.

Therefore, to exclude a custom brand:

" enabled " : false

Correct Answer:

Attribute: " enabled "

Value: false

Microsoft References

Video Indexer – Brands customization

Azure Video Indexer API – Brands

Analysis of Options:

A. Lower the confidence score threshold of App1

Would just accept more low-confidence predictions, but won’t add support for a new format. ❌

B. Lower the accuracy threshold of App1

Similar reasoning: affects prediction acceptance, not model capabilities. ❌

C. Add the additional contract format to the existing training set and retrain the model

Correct approach. You extend the training data to include the new format, retrain, and reuse the same model. ✅

D. Create a new training set and add the additional contract format

This means starting from scratch, which requires more effort. Not minimal. ❌

E. Create and train a new custom model

Same as D, higher effort than necessary. ❌

Correct Answer:

C. Add the additional contract format to the existing training set. Retrain the model.

Microsoft Reference:

Azure AI Document Intelligence – Custom models

Upload to: An Azure Storage account

Model type: Custom neural

You have 1,000 scanned hand-written survey responses with no consistent layout. The goal is to extract data while minimizing development effort.

Step 1: Upload location

For Azure AI Document Intelligence (formerly Form Recognizer), training data (images, PDFs) must be placed in a container in an Azure Storage account.

Options like Cosmos DB or Azure Files share are not valid training data sources for Document Intelligence.

Therefore, you upload the survey images to Azure Storage account.

Step 2: Model type

The surveys are handwritten and do not have a consistent layout.

Custom template models work only when the layout is fixed and consistent (e.g., invoices with the same fields in the same place).

Identity document (ID) model is a prebuilt model for passports, driver’s licenses, etc., not for surveys.

Custom neural model is designed to handle unstructured or variable-layout forms, including handwritten content. This best fits the survey scenario.

Thus, the correct configuration is:

Upload to Azure Storage account

Use Custom neural model

Correct Answer:

Upload to: An Azure Storage account

Model type: Custom neural

Microsoft References

Azure AI Document Intelligence – Custom models

Custom neural vs custom template models

Train a custom model with Azure Storage input

ï‚· Provide access to AI1 by using: A bearer token

ï‚· Connect to the deployment by using: A deployment name

To ensure only your apps can access the Azure OpenAI resource, authenticate with Microsoft Entra ID (Azure AD) and obtain a Bearer token (ideally via managed identity ). This avoids sharing long-lived API keys and lets you scope access by app identity and Azure RBAC. Microsoft’s guidance recommends token-based auth for Azure OpenAI and shows how to acquire a bearer token/managed identity token for calls

When calling the REST API , Azure OpenAI requires you to specify the deployment name to select which model deployment to use. The REST route includes {deployment-id} (deployment name) in the path, e.g., .../openai/deployments/{deployment-id}/chat/completions. This is explicitly documented as a key difference from OpenAI’s public API, which takes only a model.

Therefore:

Access method: Bearer token (via Entra ID/managed identity)

Deployment selector: Deployment name in the REST path

Microsoft References

Authenticate to Azure OpenAI with Microsoft Entra ID / Managed identity (bearer token): guidance and how-to

Azure OpenAI request header recommends token-based authentication (Bearer).

Azure OpenAI requires deployment name in API calls; REST path uses

Scenario Recap:

IoT data from 100 machines × 50 sensors × 1-minute intervals for 1 year = 5,000 time series datasets .

Requirement: Detect unusual values (outliers) to predict failures.

Correct Service:

Azure Anomaly Detector (A) is designed for time-series anomaly detection.

It learns temporal patterns and detects anomalies in sensor/IoT data streams.

Other options:

Cognitive Search = full-text search

Form Recognizer = extract text/fields from documents

Custom Vision = image classification

Answer: A. Anomaly Detector

Comprehensive Detailed Explanation

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

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

To upload an image from a stream to a person in a person group, call

faceClient.PersonGroupPerson.AddFaceFromStreamAsync(personGroupId, personId, t);

Putting it together:

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

{

Guid personId = persons[i].PersonId;

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

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

{

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

{

await faceClient.PersonGroupPerson.AddFaceFromStreamAsync(

personGroupId, personId, t);

}

}

});

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

References

Microsoft Docs – Face API: Add a face to a person ( AddFaceFromStreamAsync , AddFaceFromUrlAsync ) in PersonGroupPerson operations.

https://learn.microsoft.com/azure/ai-services/face/ (see SDK/PersonGroupPerson methods)

NET File.OpenRead returns Stream .

https://learn.microsoft.com/dotnet/api/system.io.file.openread

To build a product support chatbot from a product support manual with minimal development effort and cost, use Custom question answering (CQA) in Azure AI Language. CQA lets you ingest files (including manuals/FAQs), automatically extract Q & A, author answers where needed, and expose a ready-to-call endpoint that plugs straight into Azure Bot Service—no fine-tuning or complex retrieval stack required.

Not A (Phi-3 with fine-tuning): fine-tuning adds cost/complexity and isn’t necessary for a manual-based Q & A bot.

Not C (GPT-4 with grounding via Azure AI Search): powerful but more engineering and cost (indexing, RAG orchestration) than needed.

Not D (Document Intelligence): focuses on OCR/structured data extraction, not conversational Q & A.

References (Microsoft Docs):

Custom question answering overview and scenario (create a conversational layer over your data; supports manuals/FAQs).

CQA supports unstructured documents and quick setup in Language Studio/Azure AI Foundry.

The requirement is to extract structured information (invoice items, sales amounts, customer details) from scanned invoice images.

Form Recognizer (now part of Azure AI Document Intelligence) is specifically designed for extracting structured data from documents such as invoices, receipts, and business forms.

Custom Vision → used for image classification or object detection, not for text/data extraction.

Computer Vision → can extract text with OCR, but it doesn’t provide structured field-level extraction for invoices.

Immersive Reader → for accessibility (reading content aloud, translating text), not document data extraction.

Correct Answer: D

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 Recap:

Application: Flower classification.

Solution: Add new images and labels to the existing model. Retrain and publish the model.

Analysis:

This is the best practice workflow:

Add new labeled training images.

Retrain the model with updated dataset.

Publish the retrained model.

This clearly meets the goal.

Correct Answer: A. Yes

The requirement is to ensure the chatbot provides formal responses to spurious questions (random/off-topic).

The correct way to handle this is to add a chit-chat personality dataset (such as qna_chitchat_professional.tsv ) in Language Studio, retrain, and republish.

Modifying existing QnA pairs for custom intents (as the solution suggests) would not cover spurious/off-topic questions automatically. It only affects known knowledge base intents.

Therefore, this solution does not meet the goal.

Correct Answer: B

Step 1: For the knowledge base, select Show active learning suggestions.

In order to see the suggested questions, on the Edit knowledge base page, select View Options, then select Show active learning suggestions.

Step 2: Approve and reject suggestions.

Each QnA pair suggests the new question alternatives with a check mark, ✔ , to accept the question or an x to reject the suggestions. Select the check mark to add the question.

Step 3: Save and train the knowledge base.

Select Save and Train to save the changes to the knowledge base.

Step 4: Publish the knowledge base.

Select Publish to allow the changes to be available from the GenerateAnswer API.

When 5 or more similar queries are clustered, every 30 minutes, QnA Maker suggests the alternate questions for you to accept or reject.

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: 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.

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

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.

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

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

Box 2: /translate

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

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

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: 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.

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.

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: 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.

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 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.

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.

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.