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

Comprehensive Detailed Explanation

You’re creating a resource that will handle sentiment analysis (Language) and OCR (Vision) with a single key and endpoint. That is a Cognitive Services multi-service (Azure AI services) account.

When creating any Azure resource via ARM (HTTP/REST), the URL must be scoped to a subscription, not a tenant, and use the correct resource provider.

Scope segment: Use

https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{rg}/providers/...

Therefore, choose subscriptions/8d3591aa-96b8-4737-ad09-00f9b1ed35ad (not the tenant scope).

Resource provider: Multi-service cognitive account is under Microsoft.CognitiveServices.

The full path to create would be like:

/providers/Microsoft.CognitiveServices/accounts/{accountName}?api-version=2021-10-01

This creates a single Cognitive Services (Azure AI services) resource that provides one endpoint and one key across multiple services (e.g., Language for sentiment analysis and Vision for OCR).

Microsoft Azure AI Solution References

Azure Resource Manager: Create Cognitive Services account (Microsoft.CognitiveServices/accounts) using REST and API version 2021-10-01.

Azure AI services (Cognitive Services) multi-service resource overview—single key/endpoint across services.

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.

Scenario: Support autocompletion and autosuggestion based on all product name variants.

A: Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API usage is illustrated in the following call to the Autocomplete REST API.

POST /indexes/myxboxgames/docs/autocomplete?search & api-version=2020-06-30

{

" search " : " minecraf " ,

" suggesterName " : " sg "

}

B: In Azure Cognitive Search, typeahead or " search-as-you-type " is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for " Seattle " will have prefix combinations of " sea " , " seat " , " seatt " , and " seattl " to support typeahead.

F. Use the default standard Lucene analyzer ( " analyzer " : null) or a language analyzer (for example, " analyzer " : " en.Microsoft " ) on the field.

Parameter type: stream

Visual features list: VisualFeatureTypes.Description

Result to use: results.Description.Captions[0]

To generate accessible alt text , you should use the caption produced by Azure Computer Vision’s Description feature (it produces a human-readable sentence with a confidence score). Therefore:

The image input should be a stream , because you’re uploading images (not just passing URLs) during product creation and AnalyzeImage…Async supports image streams .

Request the Description feature in VisualFeatureTypes so the service returns results.Description.Captions.

Use results.Description.Captions[0] and return the caption text if its confidence is high enough (e.g., > 0.5) to meet the accessibility requirement that all images must have relevant alt text .

Other features (Tags, Objects, Brands) are useful for enrichment but do not directly return natural-language captions suitable for alt text.

Microsoft Azure AI Solution References

Computer Vision (Image Analysis) – Description/Captions and features: Microsoft Docs, Image Analysis – VisualFeatureTypes.Description returns description.captions.

https://learn.microsoft.com/azure/ai-services/computer-vision/concept-image-analysis

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

https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

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

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: 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

Comprehensive Detailed Explanation

The requirement:

You have an Azure AI Document Intelligence resource (formerly Form Recognizer).

You want the app to detect handwritten content inside PDF files.

Step 1 – Choosing the correct model

prebuilt-document: Extracts structure and text from documents, but not optimized specifically for handwriting.

prebuilt-contract: Designed for contracts, not general handwriting.

prebuilt-read: Extracts text (printed and handwritten) from documents. Supports handwritten content recognition.

Therefore, to analyze handwritten content, the correct choice is prebuilt-read.

Step 2 – Confidence threshold

The style.Confidence value indicates the confidence that the text is handwritten.

A typical threshold used is 0.75 (75%).

This ensures that only results with reasonably high confidence are considered handwritten.

Values like 0.1 would be too low (allowing false positives), while 1.0 might miss valid handwriting because it requires absolute certainty.

Correct Answer:

Model: prebuilt-read

Confidence threshold: 0.75

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

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

Training phrases like:

“Find contacts in London”

“Who do I know in Seattle?”

“Search for contacts in Ukraine”

Key Requirement

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

Why the Solution is Incorrect

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

This is not correct because:

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

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

Here, the intent is already defined: FindContact .

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

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

Correct Approach

Keep the existing FindContact intent.

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

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

Correct Answer: B. No

Microsoft References

Intents and entities in Language Understanding

Prebuilt entity: GeographyV2

This is because the Azure Video Indexer website allows you to upload videos from a URL or from your file system, but there are some limitations and considerations for each option1.

If you upload from your file system, the size of the file is limited to 2 GB, which is less than the size of file1.avi (20 GB). Therefore, this option is not feasible.

If you upload from a URL, the size of the file is limited to 30 GB, which is enough for file1.avi. However, the URL must be publicly accessible and valid, and the file must be accessible. You cannot use URLs from streaming services such as YouTube1. Therefore, options A and B are not valid.

The best option is to upload file1.avi to a cloud storage service such as Microsoft OneDrive, and then generate a shareable link for the file. You can then paste the link in the Azure Video Indexer website and upload the video from the URL. This way, you can avoid the file size limitation and ensure that the file is accessible and valid2.

Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.

QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data. It is used to find the most appropriate answer for any input from your custom knowledge base (KB) of information.

Text Analytics: Mine insights in unstructured text using natural language processing (NLP)—no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API evaluates text input

Semantic Kernel supports multiple prompt template languages. Handlebars and Liquid both allow you to bind complex objects (with nested/sub-properties), iterate over collections, and use conditionals—ideal when your prompt needs structured data.

JSONL is a dataset/record format, not a prompt templating language.

YAML is a configuration/serialization format; while SK can store configs in YAML, YAML itself isn’t a prompt templating engine for rendering nested objects in prompts.

“Semantic Kernel” as an option is not a template language; SK provides the runtime and also a simple legacy template syntax, but for complex/nested objects Microsoft recommends Handlebars (cross-language) or Liquid (.NET).

References (Microsoft Docs):

Handlebars prompt templates in Semantic Kernel (supports SK prompts, expressions, iteration, object binding). Microsoft Learn

Liquid prompt templates in Semantic Kernel (example shows passing objects and iterating over nested data). Microsoft Learn

SK prompt template syntax overview (context on SK templating options). Microsoft Learn

==========================

" Customer-managed keys require an additional billable service, Azure Key Vault, which can be in a different region, but under the same subscription, as Azure Cognitive Search. Enabling CMK encryption will increase index size and degrade query performance. "

same document also lists Azure Key Vault as a requirement:

https://docs.microsoft.com/en-us/azure/search/search-security-overview#data-protection

The code creates a SpeechConfig and an AudioOutputConfig with filename= " path/to/file.wav " , then constructs a SpeechSynthesizer and calls speak_text_async(input) .

Existing file behavior: AudioOutputConfig with a filename directs synthesis output to that file path; the documentation emphasizes that it specifies the output file and that the parent directory must already exist. It does not require a unique filename (i.e., it does not fail purely because a file exists). In practice, the SDK writes to the specified path. Therefore, the statement claiming failure if a file already exists is No.

Voice source: The filename in AudioOutputConfig is output, not input. To choose a voice, you set speech_config.speech_synthesis_voice_name or use SSML; the WAV file isn’t “sampled” to create a voice. Hence this statement is No. Microsoft Learn

File generation: Using AudioOutputConfig(filename=...) with SpeechSynthesizer and speak_text_async(...) writes synthesized audio to that file. Many samples show the same pattern (often with .get() to wait for completion). So the function’s purpose is to generate an audio file from the input text—Yes.

Note: In production, call .get() (or await) on speak_text_async(...) so the program waits for synthesis to complete before exiting. Microsoft Learn

References (Microsoft Azure AI Speech)

How to synthesize speech from text (create AudioOutputConfig to write to a file; synthesize with speak_text_async ).

Python AudioConfig class (audio output can be a speaker, audio file (WAV), or stream). Microsoft Learn

NET AudioConfig.FromWavFileOutput (specifies output file; parent directory must already exist). Microsoft Learn

Azure Q & A example (Python: speak_text_async(...).get() usage). Microsoft Learn

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

References

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

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

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

Azure OpenAI content filtering system and configuration overview.

Comprehensive Detailed Explanation

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

Why does migrating to a higher tier work?

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

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

Moving to a higher tier increases:

The allowed query units (QUs),

Maximum queries per second (QPS),

and available compute resources.

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

Why not other solutions?

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

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

Correct Answer: A. Yes

Microsoft References

Azure Cognitive Search service limits by tier

Scale resources in Azure Cognitive Search

Azure Cognitive Search pricing tiers

Azure AI Document Intelligence (Standard S0) enforces input constraints: images must be between 50×50 and 10,000×10,000 pixels and typical file size limits apply (images/PDFs well below hundreds of MB; an 800-MB PDF is far beyond supported sizes).

File1.pdf (800 MB) – exceeds supported size limits → cannot be analyzed.

File2.jpg (25×25 px) – below the 50×50 px minimum → cannot be analyzed.

File3.tiff (5,000×5,000 px; 5 MB) – within pixel and size limits → can be analyzed.

References

Document Intelligence service limits (file size/page and image dimension constraints).

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

Document Intelligence supported file types.

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

A user User1 must be able to upload datasets and fine-tune existing models in Azure OpenAI.

The role must follow principle of least privilege .

Role analysis:

A. Cognitive Services Contributor : Too broad; allows management of all Cognitive Services resources, not least privilege.

B. Contributor : Overly permissive; allows full management rights on the entire resource, not just OpenAI.

C. Cognitive Services OpenAI User : Grants permissions only to use models (query endpoints). Does not allow fine-tuning or dataset upload.

D. Cognitive Services OpenAI Contributor : Correct role. This provides the ability to manage OpenAI resources, including uploading training data and fine-tuning models.

Correct Answer: D. Cognitive Services OpenAI Contributor

Microsoft Reference:

Azure RBAC roles for Azure OpenAI

Built-in roles: Cognitive Services OpenAI Contributor

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.

For a table projection in a knowledge store (skillset definition), the table node must define:

tableName : the table to project into (physical expression).

generatedKeyName : the name of the system-generated key column.

source : an object-shaping expression (projection path) describing what document content flows into the table.

dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.

References

Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.

Box 1: Adaptive card

Box 2: an image

Database transactions follow the ACID properties :

Atomicity → Ensures that each transaction is treated as a single “all-or-nothing” unit of work. Either the entire transaction is completed, or none of it is applied. This guarantees that transactions are executed only once and rolled back completely if they fail.

Consistency → Ensures that a transaction brings the database from one valid state to another, preserving integrity constraints.

Isolation → Ensures that concurrent transactions do not interfere with each other.

Durability → Ensures that once a transaction is committed, it remains permanent even in case of system failure.

Thus, the correct property here is Atomicity .

Microsoft Reference: ACID properties in databases

ï‚· SpeechRecognitionLanguage must be set to " fr " (French).

ï‚· AddTargetLanguage( " de " ) must be used to specify German as the translation output.

Correct Code Completion

var speechTranslationConfig =

SpeechTranslationConfig.FromSubscription(speechKey, speechRegion);

speechTranslationConfig.SpeechRecognitionLanguage = " fr " ;

speechTranslationConfig.AddTargetLanguage( " de " );

Explanation

SpeechRecognitionLanguage → sets the source spoken language that the Speech service listens to (here, French).

AddTargetLanguage( " de " ) → adds German as the target translation language.

SpeechSynthesisLanguage would be used if you want to speak out the translation, but since the requirement is only about defining input and output languages, we use AddTargetLanguage .

Microsoft Reference

Speech translation with Speech SDK

Final Answer:

First blank → SpeechRecognitionLanguage

Second blank → AddTargetLanguage

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

The requirement is to identify the created Azure AI services account within a DevOps pipeline.

The az cognitiveservices account show command returns the details (name, id, location, etc.) of an existing Azure AI service account.

az resource link creates or manages links between resources, not what we need.

az account list lists Azure subscriptions, not resources.

az cognitiveservices account network-rule manages network access rules but does not return account identity.

Microsoft References:

az cognitiveservices account show

Batch anomaly detection is a type of anomaly detection that scans the entire dataset at once for outliers and unusual patterns. Batch anomaly detection is suitable for offline analysis of historical data, such as sensor data from the previous 24 hours. Batch anomaly detection can use various techniques, such as statistical methods, machine learning methods, or hybrid methods, to identify anomalies in the data123.

In Question Answering (Custom question answering in Azure AI Language), you improve coverage for different user phrasings by adding alternative questions to the same answer. For example, adding “How much does < product > cost?” as an alternate question to the existing Q/A about “What is the price of < product > ?” enables the service to match both user queries to the same answer. After adding alternates, you must retrain and republish the project for changes to take effect. This directly addresses the gap and meets the goal.

References

Add and manage alternative questions in custom question answering; retrain/republish for updates.

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/contribute-to-project

Custom question answering concepts and authoring workflow.

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

The statement is:

" When using the Azure Cosmos DB Gremlin API, the container resource type is projected as a ______. "

Options:

graph → Correct. The Gremlin API in Azure Cosmos DB is designed to work with graph data models, supporting vertices (nodes) and edges (relationships). A container is projected as a graph.

table → Used when working with the Table API (key/value store). Not correct here.

partition key → Refers to data distribution and scalability, not the container resource type.

document → Refers to the SQL API or MongoDB API in Cosmos DB, not Gremlin.

Therefore, the correct answer is graph.

Correct Answer: graph

Microsoft References

Azure Cosmos DB Gremlin API

Azure Cosmos DB APIs and data models

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

References

Custom question answering capabilities (no entity modeling).

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

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

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

To prevent an Azure OpenAI model (such as GPT-4 or GPT-3.5) from returning outputs that include hate speech, you must configure Azure AI Content Safety content filters on the model deployment. Content filters evaluate prompts and/or completions for categories like Hate/Unfairness, Harassment/Violence, Sexual, and Self-harm and can block or redact responses when they exceed your selected threshold. Applying a content filter to Model1 ensures that App1 will not return hate speech, satisfying the requirement.

Why the other options are incorrect:

A. Frequency penalty – discourages token/repetition in output; it does not moderate harmful content.

B. Abuse monitoring – provides telemetry/policy monitoring for potential misuse; it is not a response-time content filter and does not block specific hate content.

D. Temperature – adjusts randomness/creativity; it does not enforce safety.

Microsoft Azure AI References

Azure OpenAI Service – Safety system & content filtering (configure per deployment; categories include Hate).

Azure AI Content Safety – Overview and moderation categories.

Azure OpenAI Studio – Attach Content Safety filters to deployments (prompt/completion filtering).

Explanation

Identify the road signs and generate a short description of each road sign: Azure AI Vision is the most suitable choice for this task. It is designed for image analysis and can accurately detect and describe objects such as road signs in large datasets like your 100,000 images. This service is optimized for computer vision tasks and helps minimize costs by efficiently processing images at scale.

Analyze the descriptions to generate a report about the different types of road signs and how often each type occurred: Azure OpenAI GPT-4-Turbo is ideal for this action. It can process natural language descriptions, identify patterns, and generate a detailed report based on the frequency of different road sign types. This model is cost-effective for text analysis tasks and can handle the volume of data derived from the image descriptions while providing advanced language understanding.

The A in ACID stands for Atomicity .

Atomicity ensures that all parts of a transaction are completed. If any part fails, the entire transaction is rolled back → all-or-nothing execution.

Isolation ensures transactions do not interfere with each other.

Consistency ensures data moves from one valid state to another.

Durability ensures committed changes survive failures.

Correct Answer: B. atomicity

To make a chatbot use uploaded company data from the Chat playground (“use your data”) , your code must:

Create a chat request (not a plain completion), so you need the ChatCompletionsOptions type for the request options.

This is the options class used for chat completions across Azure OpenAI SDKs and is the one that supports attaching data sources (extensions).

Configure the Azure AI Search data source as a chat extension so the model grounds answers on your indexed documents.

In the SDK, this is done by adding an AzureCognitiveSearchChatExtensionConfiguration entry under the extensions/dataSources collection and setting properties like SearchKey and IndexName . This is the specific configuration object for Azure AI Search integration with Chat Completions.

Therefore, the two dropdowns should be completed as:

First dropdown: ChatCompletionsOptions()

Second dropdown: AzureCognitiveSearchChatExtensionConfiguration

This matches how “Use your data (RAG)” is implemented in Azure OpenAI—chat request + Azure AI Search chat extension—so the bot can answer questions using your uploaded/company data. Microsoft Learn

Microsoft Azure AI Solution References

Azure OpenAI “Use your data” (concepts): how chat uses data sources like Azure AI Search. Microsoft Learn

Azure OpenAI client library for .NET (overview of chat options and extensions support). Microsoft Learn

Chat Completions Options (SDK docs, shows dataSources/extensions concept in chat options).

Examples/discussion of AzureCognitiveSearchChatExtensionConfiguration usage with SearchKey/IndexName.

=========================

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

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

ï‚· HTTP Method: POST

ï‚· Visual Feature: imageType

Comprehensive Detailed Explanation

The question requires configuring an Azure AI Vision API request that determines if an image is clipart or a line drawing.

Step 1 – Determine the HTTP Method

The Analyze Image endpoint ( /analyze ) of Azure AI Vision expects an HTTP POST request.

GET is not valid for analysis with an image body.

PATCH is irrelevant here.

POST is correct because you are submitting image content or a URL for analysis.

Step 1: Create a new project and load sample documents

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

Step 2: Label the sample documents

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

Step 3: Train a custom model.

Finally, train a custom model.

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

Locations, Persons, Organizations are in the outputs.

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

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

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

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

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

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

wikipediaId

wikipediaLanguage

wikipediaUrl (a link to Wikipedia page for the entity)

etc.

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.

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.

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.

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.