AI-102 Questions and Answers

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

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

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

What is translated?

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

LU reference link texts

List of .lu files under a specific path.

Use " definition " for the first field and " sampleBlobUrl " for the second field

When creating a custom text category in Azure AI Content Safety, you call the PUT /contentsafety/text/categories/{categoryName} endpoint and provide a JSON body that includes:

categoryName : the identifier of your category.

definition : a natural-language description of what the category covers (e.g., “text prompts about learning advice while preparing for an exam”).

sampleBlobUrl : a pointer to labeled sample data (typically a JSONL file in Azure Storage) used to help the service understand the category.

Keys like prompt or text are used when analyzing content (for example, text:detect ), not when defining a category. Likewise, blocklistName applies to blocklist operations, and outputType is not part of the category-creation schema. Therefore, the correct payload fields to complete the cURL are definition and sampleBlobUrl .

Microsoft Azure AI References (titles only)

Azure AI Content Safety – Create or update custom text categories (REST)

Azure AI Content Safety – Custom categories concepts and training data (JSONL in blob storage)

Azure AI Content Safety – Text analysis vs. category management endpoints

The requirement: Ensure the chatbot blocks questions that attempt to circumvent built-in safety features .

This is known as a jailbreak attempt (e.g., prompt injection, role-play tricking).

Azure AI Content Safety recently added Jailbreak risk detection for precisely this purpose.

Other options:

Protected material detection → for copyright/IP detection.

Monitor online activity → not related to OpenAI safety.

Moderate text content → general harmful content moderation (violence, hate, sexual, self-harm), but not jailbreaks.

Microsoft Reference:

Azure AI Content Safety jailbreak risk detection

You can use the Speech service to voice-enable a chat bot.

The Direct Line Speech channel uses the text-to-speech service, which has neural and standard voices.

You ' ll need to make a small configuration change so that your bot can communicate with the Direct Line Speech channel using web sockets.

You are adding a custom plugin to a Semantic Kernel–based agent and you want function calling to be enforced and self-completing:

To register a plugin that exposes one or more callable functions to the agent, use CreatePluginFromFunctions . This builds a plugin from Kernel functions (prompt- or code-based) and makes them available for tool/function calling by the model.

To ensure the model must call functions (and request any required parameters it doesn’t have), set the prompt execution settings to FunctionChoiceBehaviour.Required . In Semantic Kernel, this corresponds to the OpenAI tool/function-calling control that forces the assistant to invoke appropriate functions. When required parameters are marked in the function’s schema, the assistant will ask follow-up questions to collect them before (or while) invoking the function.

References (Microsoft Azure AI Solution)

Semantic Kernel: Plugins and Functions, ChatCompletionAgent, OpenAIPromptExecutionSettings, and FunctionChoiceBehavior for enforced tool/function calling.

To “recognize geographic locations” in Azure AI Search enrichment, add the EntityRecognitionSkill to your skillset. This built-in cognitive skill performs named entity recognition (NER) and extracts entities such as locations, people, and organizations from text.

EntityLinkingSkill links already-recognized entities to a knowledge base (e.g., Wikipedia) and is not required just to detect locations.

DocumentExtractionSkill parses file content/structure.

AzureOpenAIEmbeddingSkill creates vector embeddings, not entities.

Microsoft References

Azure AI Search – Entity recognition skill (extracts entities including locations).

Azure AI Search – Entity linking skill (disambiguates entities to a knowledge base; not for initial recognition).

Code Recap

static void GetKeyPhrases ( TextAnalyticsClient textAnalyticsClient, string text)

{

var response = textAnalyticsClient.ExtractKeyPhrases(text);

Console.WriteLine( " Key phrases: " );

foreach ( string keyphrase in response.Value)

{

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

}

}

Then called with:

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

Statement Analysis

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

Yes.

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

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

No.

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

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

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

No.

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

Final Answer

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

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

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

Microsoft References

Azure Text Analytics – Key Phrase Extraction

TextAnalyticsClient.ExtractKeyPhrases

account

The statement is:

" When provisioning an Azure Cosmos DB ______, you need to specify which type of API you will use. "

Options:

account → Correct. When you create a Cosmos DB account, you must choose the API type (e.g., SQL API, MongoDB API, Cassandra API, Table API, Gremlin API). This decision defines the data model and query language. ✅

container → A container is created inside a database (tables, collections, graphs) but does not determine the API type.

database → Exists inside an account and inherits the API type from the account.

item → Represents individual records/documents; not where the API is chosen.

Thus, the API choice is made at the account level.

Correct Answer: account

Microsoft References

Azure Cosmos DB account overview

Choose the right API for Cosmos DB

 Provide access to AI1 by using: A bearer token

 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

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.

Scenario Recap:

Proposed Solution: Deploy service1 with a public endpoint, and configure a Network Security Group (NSG) for vnet1.

Analysis:

NSGs only control inbound/outbound traffic at the VNet/subnet or NIC level.

Configuring NSGs does not create private connectivity to Cognitive Search.

Traffic still flows over the public endpoint.

Answer: B. No

NSGs filter traffic, but they don’t prevent routing via the public internet. Only Private Link does.

The chatbot needs to recognize two different kinds of information:

Code names for internal product development

These are not standard entities like " locations " or " organizations " that prebuilt NER can detect.

You need to train a custom model to recognize internal terms or codenames (for example, " Project Falcon " ).

The correct Azure Cognitive Service for Language feature is Custom Named Entity Recognition (Custom NER), which allows defining and training entity categories specific to your business.

Messages that include credit card numbers

Credit card numbers are sensitive data falling under Personally Identifiable Information (PII).

Azure Cognitive Service for Language provides a PII detection feature that automatically identifies and masks sensitive information such as credit card numbers, SSNs, and phone numbers.

This minimizes development effort since it is prebuilt and ready to use.

Correct Answer Mapping:

Identify code names for internal product development → Custom named entity recognition (NER)

Identify messages that include credit card numbers → Personally Identifiable Information (PII) detection

Microsoft References

Custom Named Entity Recognition in Azure AI Language

PII detection in Azure AI Language

You are using the Azure OpenAI .NET SDK to send a prompt (“What is Microsoft Azure?”) to a text model deployment. For text generation, you must call the Completions endpoint; hence client.GetCompletions(...) (or the async variant) is correct.

The Response < Completions > object returns one or more choices; the generated text is contained in response.Value.Choices[0].Text , which you can write to the console.

GetEmbeddings would return vector embeddings, not natural-language text, and GetImageGenerations is for DALL·E image generation, so neither meets the requirement to “answer user questions” and print text.

Microsoft References

Azure OpenAI Service (.NET) – Completions quickstart and sample showing GetCompletions and reading Choices[0].Text .

Azure OpenAI Service – Embeddings overview (returns vectors, not text).

Azure OpenAI Service – Image generation overview (DALL·E, not for text answers).

Explanation

curl -i -X: The POST method is appropriate because you are creating a new custom analyzer, which involves sending a request to create a resource. POST is used for creating new resources in REST APIs.

https:// < cu1 > .services.ai.azure.com/: The base URL should be updated to include the contentunderstanding endpoint, as this is the specific service within the Azure AI Content Understanding resource (cu1) that handles analyzer-related operations. Replace < cu1 > with the actual resource name or endpoint provided by your Azure subscription.

/analyzers/myInvoice7api-version-2024-12-01-preview: This part of the URL is correct as it specifies the endpoint for creating a custom analyzer (/analyzers) with the name myInvoice7 and the API version 2024-12-01-preview. No changes are needed here, assuming the analyzer name and version are intended as shown.

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

You have files with the following formats:

File1: WMV (34 mins, 400 MB)

File2: AVI (90 mins, 1,200 MB)

File3: MOV (300 mins, 980 MB)

File4: MP4 (80 mins, 1,800 MB)

You must determine which files can be uploaded to Azure AI Video Indexer.

Key Rules (Video Indexer upload limitations):

Supported formats: MP4, MOV, WMV, AVI, M2TS.

Maximum file length: 4 hours (240 minutes).

Maximum file size: 2 GB (2048 MB) for website uploads.

Check each file:

File1 (WMV, 34 min, 400 MB): Supported format, within time and size → ✅ Allowed.

File2 (AVI, 90 min, 1,200 MB): Supported format, within time and size → ✅ Allowed.

File3 (MOV, 300 min, 980 MB): Supported format, but exceeds 240 min limit → ❌ Not Allowed.

File4 (MP4, 80 min, 1,800 MB): Supported format, within size (under 2 GB), within time → ✅ Allowed.

Correct Answer:

A. File1, File2, and File4 only

Microsoft Reference:

Video Indexer upload limitations

You need to monitor a video stream while ensuring the microphone stays enabled and detect when the user asks a question. The simplest/lowest-effort approach is to use Azure AI Speech-to-text to transcribe the user’s audio in real time, then apply a lightweight check (e.g., interrogatives like “who/what/how” or final “?”) to detect a question. Object detection or the Face service analyze visual content (objects/faces/emotions) and cannot tell whether a spoken utterance is a question. Language detection only identifies which language is being spoken; it does not transcribe or classify questions.

References

Azure AI Speech: real-time speech-to-text (streaming) for transcription and downstream analysis.

https://learn.microsoft.com/azure/ai-services/speech-service/speech-to-text

Face service overview (not for speech/question detection).

https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity

Custom Vision object detection overview (visual objects, not spoken questions).

https://learn.microsoft.com/azure/ai-services/custom-vision-service/object-detection-overview

Language detection capability description (identifies language only).

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

1. api-nam.cognitive.microsofttranslator.com

2. translate

https://learn.microsoft.com/en-us/azure/cognitive-services/Translator/reference/v3-0-reference#base-urls

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

https://learn.microsoft.com/en-us/azure/cognitive-services/translator/reference/rest-api-guide

- translate

Translate specified source language text into the target language text.

To generate less deterministic and more creative responses with Azure OpenAI chat completions, increase the Temperature setting. Temperature controls sampling randomness: higher values (e.g., ~0.8–1.0) yield more diverse/creative outputs; lower values (~0–0.2) are more deterministic.

The message being sent in the Messages collection is the user’s prompt, so the correct role is ChatRole.User . The role does not control creativity; it only tells the model who authored the message (system/assistant/user/function). Creativity is governed by parameters such as Temperature (and, to a lesser extent, TopP ). PresencePenalty biases against repeating seen tokens/topics but does not primarily increase creativity in the intended way.

So the code should look like:

new ChatCompletionsOptions()

{

Messages =

{

new ChatMessage(ChatRole.User, @ " < user prompt here > " ),

},

Temperature = ( float ) 1.0 ,

MaxTokens = 800 ,

};

Microsoft Azure AI Solution References

Azure OpenAI Service – Chat Completions parameters (Temperature controls randomness/creativity; higher = more diverse results).

Azure OpenAI Service – Message roles in chat ( system , user , assistant , function ) and their purposes.

Azure OpenAI Service – Presence/Frequency penalties vs. Temperature (penalties reduce repetition; temperature changes creativity).

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

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

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

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

Box 2: /translate

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.

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: 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.

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.

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.

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

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: 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.

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.