Free and Premium Microsoft AI-102 Dumps Questions Answers

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: 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.

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

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

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

{

"search": "minecraf",

"suggesterName": "sg"

}

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

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

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

e/v3-0-reference

Box 2: /translate

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.

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

Comprehensive Detailed ExplanationThe 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]

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards

Microsoft References

In the Azure AI Translator .NET SDK (Azure.AI.Translation.Text), the TextTranslationClient exposes TranslateAsync overloads that accept a target language (e.g., "fr") and an input string to translate. The call returns a Response>. Each TranslatedTextItem includes the automatically detected source language (DetectedLanguage.Language) and a collection of translations in Translations. To print the translated text for the first target language, read translation.Translations[0].Text.

Therefore:

Use TranslateAsync(targetLanguage, input) to perform the translation.

Output translation.Translations[0].Text to display the translated result.

Microsoft References

TextTranslationClient.TranslateAsync method (overloads, usage). Microsoft Learn

TextTranslationClient class overview. Microsoft Learn

TranslatedTextItem model and Translations property (array of translation results).

Quickstart: Translator text client libraries (end-to-end usage pattern). Microsoft Learn

The app must:

Identify road signs and extract text from images

This is an OCR (Optical Character Recognition) task.

Azure AI Vision provides OCR capabilities to detect and extract text from images (including road signs).

Correct choice: Azure AI Vision.

Analyze the text to identify well-known locations

Once text is extracted, you need to detect if it refers to well-known places (e.g., cities, landmarks).

This is a Named Entity Recognition (NER) problem.

Azure AI Language service includes entity recognition for identifying locations, people, organizations, etc.

Correct choice: Azure AI Language.

Other options:

Azure AI Document Intelligence → Focused on structured documents (invoices, forms), not road signs.

Azure AI Search → Useful for indexing and search, not text extraction or entity recognition.

Correct Answer Matrix:

Extract the text → Azure AI Vision

Identify well-known locations → Azure AI Language

Azure AI Vision – OCR text extraction

Azure AI Language – Named Entity Recognition (NER)

Microsoft References

For consistent, deterministic answers about policy PDFs with minimal effort, use Azure AI Language – Question Answering (Custom Question Answering). You can ingest PDFs into a knowledge base and define/curate the exact answers. The service then returns the same answer for the same question every time (subject to your KB content and configuration), avoiding the variability of generative models and custom ML. Microsoft Azure+2

Microsoft References:

What is Custom Question Answering? (Language service). Microsoft Learn

Azure AI Language – Question Answering product page. Microsoft Azure

Azure AI Language overview (includes Question Answering feature)

Step 1: Export the model using the Export for containers (GZIP) option.

Export versioned app's package from LUIS portal

The versioned app's package is available from the Versions list page.

Sign on to the LUIS portal.

Select the app in the list.

Select Manage in the app's navigation bar.

Select Versions in the left navigation bar.

Select the checkbox to the left of the version name in the list.

Select the Export item from the contextual toolbar above the list.

Select Export for container (GZIP).

The package is downloaded from the browser.

Step 2: Select v1.1 of app1.

A trained or published app packaged as a mounted input to the container with its associated App ID.

Step 3: Run a contain and mount the model file.

Run the container, with the required input mount and billing settings.

When deploying an Azure OpenAI model using the Microsoft.CognitiveServices/accounts/deployments resource in an ARM template, the throughput for a Standard deployment is set on the SKU object. Specifically:

Use the capacity field of the SKU to specify the per-deployment rate.

Provide the integer value that represents the requests per minute (RPM) you want the deployment to handle.

Therefore, to meet the requirement of 600 requests per minute, complete the template as:

"sku": {

"name": "Standard",

"capacity": 600

}

This uses a single deployment SKU with a defined capacity. The ARM schema for accounts/deployments explicitly includes sku.capacity for resources that support scale (Azure OpenAI deployments do), and Azure OpenAI quota/rate-limit guidance confirms that rate limits are assigned per deployment.

Microsoft Azure AI Solution References

ARM template—Microsoft.CognitiveServices/accounts/deployments (2023-05-01): property reference showing sku.capacity. Microsoft Learn

Resource type reference—latest accounts/deployments: confirms SKU usage on deployments. Microsoft Learn

Azure OpenAI quota and rate limits: explains assigning deployment-level limits (RPM/TPM). Microsoft Learn

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.

1. api-nam.cognitive.microsofttranslator.com

2. translate

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

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

- United States

api-nam.cognitive.microsofttranslator.com

- translate

Translate specified source language text into the target language text.

The D in ACID stands for Durability.

Durability guarantees that once a transaction is committed, the changes are permanent, even in the case of power loss, crashes, or OS restarts.

Atomicity = all-or-nothing.

Consistency = valid state transitions.

Isolation = transactions run independently without interference.

Correct Answer: A. durability

Data visualization’s primary purpose is to make data understandable and meaningful through visuals such as charts, dashboards, and graphs.

It helps to communicate significance (option A) and show patterns, correlations, and trends over time (option B).

C. Implement machine learning to predict future values → Not visualization, but rather ML/AI.

D. Enforce business logic across reports → Business logic is handled in data modeling or ETL, not visualization.

Correct Answers: A and B

Box 1: Yes

You create property accessors using the CreateProperty method that provides a handle to the BotState object. Each state property accessor allows you to get or set the value of the associated state property.

Box 2: Yes

Box 3: No

Before you exit the turn handler, you use the state management objects' SaveChangesAsync() method to write all state changes back to storage.

Comprehensive Detailed ExplanationThe question focuses on monitoring a public-facing sales system that processes video and text, ensuring that the system provides equitable results regardless of user location or background.

Two Microsoft Responsible AI principles apply directly here:

Fairness

AI systems should treat all people fairly and avoid bias.

Monitoring ensures that outcomes do not disadvantage people based on location, background, or demographics.

This directly addresses the requirement for equitable results.

Inclusiveness

AI systems should empower everyone and engage people of all backgrounds.

Monitoring for inclusiveness ensures that the system works well across different regions, languages, and cultures, making the system accessible and beneficial for all users.

A. Transparency

Important for explaining how AI systems work, but does not directly address equitable results across demographics.

D. Reliability and safety

Focuses on consistent and safe system operation, not equity across users.

E. Privacy and security

Critical for data protection, but not related to equitable treatment of users.

Why not the other options?

Correct Answer:

B. fairness

C. inclusiveness

Microsoft Responsible AI Principles

Fairness in AI systems

Inclusiveness in AI systems

Microsoft References

Explanation:

The given text:

"Our tour guide took us up the Space Needle during our trip to Seattle last week."

The Language service output shows:

Text

Category

ConfidenceScore

Tour guide

PersonType

0.45

Space Needle

Location

0.38

Trip

Event

0.78

Seattle

Location

0.78

Last week

DateTime

0.80

NER identifies named entities (people, places, dates, events, organizations, etc.) in text and classifies them into categories with confidence scores.

In the table, we see categories such as PersonType, Location, Event, DateTime → these are classic outputs of NER.

A. Entity Linking → Identifies entities and links them to a knowledge base (like Wikipedia). Output would include links/IDs rather than just categories.

C. Key Phrase Extraction → Returns important phrases (“tour guide”, “Space Needle”, “trip”, “Seattle”, “last week”) but does not assign categories like PersonType, Location, DateTime.

D. Sentiment Analysis → Returns sentiment scores (positive/neutral/negative), not entity categories.

Why the correct answer is Named Entity Recognition (NER):Why not the others:

Correct Answer:B. Named Entity Recognition

Named Entity Recognition in Azure AI Language

Entity Linking vs Named Entity Recognition

Microsoft References

The scenario involves storing text files, videos, audio streams, and virtual disk images, along with metadata and a unique ID. This exactly matches Object storage characteristics:

Stores large amounts of unstructured data (blobs).

Each object contains the data itself, metadata, and a unique identifier.

Azure Blob Storage is Microsoft’s object storage solution.

Other options:

Columnar → Used for analytics workloads, optimized for reading columns (e.g., data warehouses).

Key/Value → Good for simple lookups but not optimized for storing large multimedia files.

Document → Stores semi-structured JSON documents, not large binary objects.

Thus, the correct answer is Object storage.

Microsoft Reference: Azure Blob Storage (Object storage) overview

You created five bots with Microsoft Bot Framework Composer, but want to make them appear as one unified bot for users, with dynamic routing depending on user input.

This is exactly the Bot Framework Orchestrator scenario. Orchestrator is the recognizer that allows you to dispatch incoming utterances to the correct skill (bot) or dialog.

Step 1 – Install Orchestrator package

To use Orchestrator inside Bot Framework Composer, you first install the Orchestrator package.

This provides the functionality for language understanding and routing.

Correct answer: F.

Step 2 – Change Recognizer/Dispatch type

By default, a bot project might use LUIS recognizer or Regex recognizer.

To use Orchestrator, you need to change the recognizer type to Orchestrator in Composer.

Correct answer: B.

Step 3 – Create an Orchestrator model

Orchestrator requires a trained model that maps utterances to intents or routes them to the correct skill/bot.

You must create an Orchestrator model for dispatch.

Correct answer: A.

Other Options (Incorrect):

C. Create a Composer extension → Not required for orchestrating multiple bots.

D. Enable WebSockets → Useful for streaming scenarios, not for bot orchestration.

E. Create a custom recognizer JSON file → Applies to building custom recognizers, not needed with Orchestrator.

The chatbot uses Question Answering in Azure Cognitive Service for Language.

Users report that the chatbot’s responses to spurious questions (such as jokes, casual chitchat, or off-topic queries) are not formal enough.

Comprehensive Detailed ExplanationIn Question Answering projects, you can add chitchat sources provided by Microsoft to handle small talk. These sources include:

qna_chitchat_friendly.tsv → Informal/friendly responses.

qna_chitchat_professional.tsv → Formal/professional responses.

qna_chitchat_witty.tsv → Humorous/witty responses.

By changing the chitchat source to qna_chitchat_professional.tsv, then retraining and republishing, the bot will provide formal responses to spurious questions.

Therefore, this solution meets the goal.

Correct Answer: A. Yes

Add chit-chat to a Question Answering project

Chit-chat personality types (friendly, professional, witty)

Microsoft References

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

You have a chatbot that uses Azure OpenAI for generative responses and Azure AI Content Safety to filter objectionable content.

The requirement is to optimize the content filter configurations by running tests on sample questions.

The proposed solution: Using Content Safety Studio → Moderate text content feature.

Comprehensive Detailed ExplanationThis solution is correct because:

Content Safety Studio provides tools to test and fine-tune content filters.

The Moderate text content feature allows you to run sample prompts and responses against filters for categories such as sexual, violence, hate, and self-harm.

This is exactly how you optimize and validate configurations before applying them to production workloads.

Correct Answer: Yes

Microsoft Reference:

Content Safety Studio overview

Azure AI Content Safety

Platform as a service (PaaS) database offerings in Azure require less setup and configuration effort than Infrastructure as a Service (IaaS) database offerings.

Yes

PaaS abstracts away the OS, patching, backups, and much of the infrastructure management. IaaS requires users to configure and manage VMs and databases manually.

Platform as a service (PaaS) database offerings in Azure provide end users with the ability to control and update the operating system version.

No

In PaaS, the OS and platform management are handled by Azure. Users cannot directly control or update the OS version. This is a responsibility difference between IaaS and PaaS.

All relational and non-relational platform as a service (PaaS) database offerings in Azure can be paused to reduce costs.

No

Not all PaaS database services support pausing. For example, Azure SQL Database serverless can auto-pause, but Azure Cosmos DB and provisioned SQL databases cannot. Hence this statement is incorrect.

Yes

No

No

Correct Responses:

Azure SQL Database – PaaS overview

What is PaaS?

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

Microsoft References

In user want to change to deploy offline model

1. Change model domain to compact model

2. Retrain compact model

3. Export model

“Safety metaprompt” (system message) helps you craft safer prompting/behavior guidance for models. While useful for improving responses, it’s separate from configuring and testing Content Safety content filters (category thresholds, input/output filtering). Using Safety metaprompt alone doesn’t test or optimize filter configurations on sample prompts. Microsoft Learn

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 cost?”. The correct fix is to author additional alternate phrasings (as in Q266).

References

Custom question answering capabilities (no entity modeling).

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

Comprehensive Detailed ExplanationYou’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 /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).

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.

Microsoft Azure AI Solution References

The requirement is to allow User1 to add custom data sources to an Azure OpenAI resource (AI1).

Following the principle of least privilege means granting only the specific rights required.

The Cognitive Services OpenAI Contributor role allows management of Azure OpenAI resources, including attaching custom data sources for RAG (retrieval-augmented generation).

The Cognitive Services Contributor role is broader and grants access to all cognitive services, which violates least privilege.

Search Service Contributor and Search Index Data Contributor are roles for Azure AI Search, not for managing data sources on Azure OpenAI.

Microsoft References:

Azure built-in roles: Cognitive Services OpenAI Contributor

a tabular form of rows and columns

Relational data is, by definition, data that adheres to the relational model.

In the relational model, data is organized into tables, which consist of rows (records or tuples) and columns (fields or attributes). This is known as the tabular form.

The other options are incorrect:

Unstructured data is the opposite of relational data.

A hierarchical folder structure is used for file organization, not the logical structure of relational data.

Comma-separated value (CSV) files can contain relational data, but the core definition of relational data is the tabular structure, not the file format itself.

Rationale

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

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

Bar chart shows simple comparisons, not running totals.

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

Correct Answer: A

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

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

Document crackingThe 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 mappingsNext, 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 executionBuilt-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 mappingsBecause 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 indexFinally, 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

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

The sample utterances:

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

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

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

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

Intents needed:

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

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

Entities needed:

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

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

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

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

Breakdown:Why not the others:

Correct Answer:D. a change setting intent

E. a query setting intent

F. a device entity

Intents and entities in CLU

CLU model design best practices

Microsoft References

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

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

Fluentd is for logging.

Http Proxy is optional for outbound connectivity.

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

Correct Answer: B

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

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.

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.

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..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

Azure AI Document Intelligence – Confidence scores

Human-in-the-loop for Document Intelligence

Microsoft References

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.

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.

C: Analyze Receipt - Get Analyze Receipt Result.

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

Request URL:

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

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

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

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

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

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

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

Explanation:

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.

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.

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

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.