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

Non-relational databases (NoSQL) typically store semi-structured or unstructured data and have the following characteristics:

Entities are often self-describing, meaning they store their own schema within the data (e.g., JSON documents).

Schema is flexible, allowing for changes without restructuring the entire database.

Other options:

Full support for Transact-SQL → This is a characteristic of relational (SQL) databases, not NoSQL.

A fixed schema → Also a relational database characteristic.

Correct Answer: self describing entities

The agent will reason over the uploaded file.●○

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

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

 The agent will reason over the uploaded file.

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

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

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

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

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

Add the prebuilt domain ToDo

Add a new application

Train the application

Publish the application

Correct Sequence:

Comprehensive Detailed ExplanationYou are building a Conversational Language Understanding (CLU) model for a chatbot that supports task tracking. To minimize development time, the best approach is to use a prebuilt domain (like ToDo) instead of building intents/entities manually.

Steps in correct order:

Add the prebuilt domain ToDo

Prebuilt domains in CLU provide ready-to-use intents and entities for common scenarios (e.g., calendar, email, ToDo).

This significantly reduces development time since you don’t need to create all intents/entities manually.

Add a new application

You must first create a CLU application that uses the prebuilt domain.

The application acts as the container for intents, entities, and utterances.

Train the application

Training is required after adding intents, entities, and utterances (or using a prebuilt domain).

This ensures the model can classify and extract information correctly.

Publish the application

Once trained, you must publish the application so it can be accessed via an endpoint and integrated into the chatbot.

Add example utterances → Not required if you’re using the prebuilt ToDo domain (utterances are already included).

Incorrect Actions in this Case

Add the prebuilt domain ToDo

Add a new application

Train the application

Publish the application

Correct Sequence:

Conversational Language Understanding (CLU) overview

Prebuilt domains in CLU (LUIS ToDo example)

Train and publish CLU models

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

Azure Storage provides multiple services, each with different data models:

Azure Files

Provides fully managed file shares accessible via SMB/NFS.

Works like a traditional file system (folders and files).

Not key/value.

Azure Blob Storage

Object storage for unstructured data like images, videos, backups, logs.

Identified by container + blob name.

Not strictly a key/value store (though blob name acts as a key).

Azure Table Storage ✅

A NoSQL key/value store for structured, non-relational data.

Data is stored in tables as entities (rows), with properties (columns).

Entities are indexed by PartitionKey + RowKey, forming a unique key/value pair.

Specifically designed for scalable key/value lookups.

Azure Queue Storage

Provides message queueing for communication between components.

Stores ordered messages, not key/value pairs.

Correct Answer: C. Azure Table

Azure Table Storage overview

Azure Storage services comparison

Microsoft References

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

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

The input string is:

"the quick brown fox jumps over the lazy dog"

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

Therefore, the returned list of key phrases will include:

"Quick brown fox"

"Lazy dog"

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

Quick brown fox

Lazy dog

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

Correct Answer: C. Quick brown fox lazy dog

Azure AI Language – Key phrase extraction

TextAnalyticsClient.ExtractKeyPhrases method (Azure.AI.TextAnalytics)

Microsoft References

The Cognitive Services OpenAI User role grants least-privilege read/use permissions: view resource endpoints, list available models, use existing deployments in Azure OpenAI Studio (e.g., generate text and images). It cannot create or manage deployments (that would require Cognitive Services OpenAI Contributor), satisfying least-privilege.

References

Azure OpenAI built-in roles: OpenAI User vs OpenAI Contributor.https://learn.microsoft.com/azu re/ai-services/openai/how-to/role-based-access-control

Azure RBAC for Azure AI services.https://learn.microsoft.com/azure/ai-services/role-based-access-control?tabs=azure-portal

To create a resource that can generate fictional stories from prompts, you need an Azure OpenAI account (--kind OpenAI).

To protect data with a customer-managed key (CMK), you must pass the key information (Key Vault URI, key name, key version) via the --encryption parameter when creating the Cognitive Services account. The JSON shown (with keyName, keyVersion, and keyVaultUri) is exactly the payload expected by --encryption.

A complete example pattern:

az cognitiveservices account create \

-n myresource -g myResourceGroup -l westeurope \

--kind OpenAI --sku S \

--assign-identity \

--encryption '{

"keyName":"keyname",

"keyVersion":"secretVersion",

"keyVaultUri":"https:// .vault.azure.net/"

}'

(Adding --assign-identity allows the service to access the Key Vault key; the hotspot specifically asks for the parameter that takes the JSON block and the resource kind, which are --encryption and OpenAI.)

Microsoft References

Azure CLI: Create Cognitive Services account with customer-managed keys using --encryption.https://learn.microsoft.com/azure/ai-services/cognitive-services-encryption-keys#configure-customer-managed-keys

Azure Cognitive Services (Azure AI services) CLI parameters including --kind OpenAI.https://learn.microsoft.com/azure/ai-services/cognitive-services-management-api-cli#create-a-cognitive-services-account

Dimensional models and data warehouses are designed for OLAP workloads (aggregations, analytics, reporting).

OLTP is for transaction-based line-of-business applications.

Batch processing is about moving and transforming data, not querying.

Stream processing handles continuous real-time data, not historical warehouse queries.

Correct Answer: B

A. Lower the confidence score threshold of App1

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

B. Lower the accuracy threshold of App1

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

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

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

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

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

E. Create and train a new custom model

Same as D, higher effort than necessary. ❌

Analysis of Options:

Correct Answer:

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

Microsoft Reference:

Azure AI Document Intelligence – Custom models

The requirement is:

Bot asks for a date input.

Bot repeats the question until a valid date is provided.

Bot allows cancellation of transaction.

This is exactly what Prompts in Bot Framework are designed for.

Example: DateTimePrompt asks for a date, validates the input, and automatically re-prompts if invalid.

Waterfall is for multi-step dialog flows, but the specific requirement is repeating until valid input — handled by Prompts.

Adaptive and Action dialogs are not relevant here in SDK context.

Correct Answer: A

In the Microsoft Bot Framework SDK, bots communicate using activities (messages, events, conversation updates, etc.).

To respond to events (like conversation started, member added, or custom event activities), you use an ActivityHandler.

The ActivityHandler class provides methods you can override, such as OnMessageActivityAsync, OnMembersAddedAsync, or custom event handlers, to send custom text responses.

Other options:

Adaptive card (A) → used for rich UI responses, not for handling events.

Dialog (C) → used for managing conversation flow, not event handling.

Skill (D) → used to integrate another bot’s functionality, not for handling custom events.

Correct Answer: B

Comprehensive Detailed ExplanationThe requirement is:

Implement a relational database in Azure.

Minimize ongoing maintenance.

Let’s analyze each option:

A. SQL Server on Azure Virtual Machines

This is an IaaS solution.

You (the customer) are responsible for OS patching, SQL Server installation, backups, updates, and maintenance.

High maintenance burden → not the right choice.

B. Azure SQL Database

This is a PaaS (Platform as a Service) offering.

Microsoft manages infrastructure, patching, backups, and high availability.

You only manage the schema, tables, and data.

Best fit for minimizing maintenance.

C. Azure HDInsight

This is a big data and analytics service, designed for Hadoop, Spark, Hive, etc.

Not a relational database. → incorrect.

D. Azure Cosmos DB

This is a globally distributed NoSQL database (supports multiple APIs: SQL API, MongoDB API, Cassandra API, etc.).

While it reduces maintenance, it is not a relational database. → incorrect.

Correct Answer: B. Azure SQL Database

What is Azure SQL Database?

SQL Server on Azure VMs vs. Azure SQL Database

Microsoft References

You are creating an SSML file for a text-to-speech app using a custom neural voice.

Requirements:

Expresses a calm tone → corresponds to style (e.g., "gentle").

Imitates the voice of a young adult female → corresponds to role (e.g., "YoungAdultFemale").

Key Knowledge:In Speech Synthesis Markup Language (SSML) with Microsoft Azure TTS:

supports attributes such as style, role, styledegree, voice, etc.

role → controls persona/age/gender (e.g., "YoungAdultFemale").

style → controls the speaking tone/intonation (e.g., "gentle", "calm", "chat").

Code Completion:

How can I assist you?

First dropdown: role

Second dropdown: style

Verified Answer:

Microsoft References:

Azure Speech SSML – express-as element

Speech synthesis styles and roles

✅ Final Answer:

role = "YoungAdultFemale"

style = "gentle"

Box 1: Yes

http://localhost:5000/status : Also requested with GET, this verifies if the api-key used to star t the container is valid without causing an endpoint query.

Box 2: Yes

The command saves container and LUIS logs to output mount at C:\output, located on container host

Box 3: Yes

http://localhost:5000/swagger : The container provides a full set of docum entation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required.

Your app uses the primary admin key for index management. If it’s suspected compromised, the least-downtime rotation is:

Regenerate the secondary admin key to ensure it’s known only to you.

Switch the app to use the secondary key (no downtime—primary still works until you cut it off).

Regenerate the primary key to invalidate the compromised credential.Query keys are read-only and cannot manage indexes, so switching to a query key would not meet the requirement. Regenerating the primary first would break the app until you update it.

References (Microsoft Azure Cognitive Search)

Admin (primary/secondary) vs. query keys and key rotation; key regeneration invalidates only the selected key.

Guidance to use the alternate admin key while rotating the other to avoid downtime.

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

Azure AI Document Intelligence offers pre-built models for common document types like invoices, which extract key fields (e.g., vendor, amount, date) with minimal setup. For custom forms like internal authorization requests, the pre-built general document model handles layout and text extraction, but to minimize development effort, use the pre-built invoice model where applicable and a custom model only if pre-built options fall short. However, the standard recommendation prioritizes pre-built for both to avoid training.

To make the chatbot respond only when the answer’s confidence is ≥ 95%, you must set the request body property confidenceScoreThreshold to 0.95 in the Query knowledge bases operation of Custom question answering. The REST API for querying a Custom question answering project is:

POST {Endpoint}/language/:query-knowledgebases?projectName={projectName}&deploymentName={deploymentName}&api-version=2021-10-01

Here, projectName identifies the Custom question answering project you’re querying (the screenshot’s placeholder ), while deploymentName specifies which deployment (for example, production) to use. The request body includes confidenceScoreThreshold (0–1) to filter out answers below that score; setting it to 0.95 enforces the requirement.

Example snippet:

curl -X POST "$LANGUAGE_ENDPOINT/language/:query-knowledgebases?projectName=&deploymentName=production&api-version=2021-10-01" \

-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \

-H "Content-Type: application/json" \

-d '{

"question": "How much energy does my phone have left?",

"confidenceScoreThreshold": 0.95

}'

References

REST API: Question Answering – Get Answers (shows endpoint with projectName/deploymentName and the confidenceScoreThreshold request property). Microsoft Learn

REST API (newer version) parameter list for projectName/deploymentName. Microsoft Learn

Ingest → Azure Data Factory

Store → Azure Data Lake Storage (already provided in the diagram)

Preprocess & model → Azure Synapse Analytics

OLAP → Azure Analysis Services (already provided in the diagram)

Reporting → Microsoft Power BI (already provided in the diagram)

Comprehensive Detailed ExplanationWe’re building a modern data analytics pipeline with Azure services. Let’s analyze each stage:

Ingest (Logs, structured data, etc. → Service)

The ingestion service must orchestrate and move data from multiple sources into the storage layer.

Azure Data Factory is designed for data ingestion and ETL pipelines.

Correct match: Azure Data Factory.

Store (Azure Data Lake Storage)

Already provided.

Serves as the central repository for both structured and unstructured data.

Preprocess & model (Service)

At this stage, raw data from Data Lake must be cleaned, transformed, and prepared for analytics.

Azure Synapse Analytics integrates with Data Lake and supports big data processing, advanced analytics, and modeling.

Correct match: Azure Synapse Analytics.

OLAP (Azure Analysis Services)

Already provided.

Provides semantic modeling, aggregations, and OLAP cubes for analysis.

Reporting (Microsoft Power BI)

Already provided.

Visualizes insights for business users.

Azure Cognitive Search: Used for indexing and searching unstructured data (e.g., documents, knowledge mining), not for ingestion or preprocessing.

Azure Data Catalog: Used for metadata management and data discovery, not for ingestion, preprocessing, or modeling in this architecture.

Incorrect Options and Why

Final Answer Mapping:

Ingest → Azure Data Factory

Store → Azure Data Lake Storage

Preprocess & model → Azure Synapse Analytics

OLAP → Azure Analysis Services

Reporting → Microsoft Power BI

Azure Data Factory – Data Integration Service

Azure Data Lake Storage – Scalable data lake

Azure Synapse Analytics overview

Azure Analysis Services overview

Power BI overview

Microsoft References

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

The requirement:

You are using Azure AI Anomaly Detector.

You need to deploy it on Server1, which has intermittent internet access.

This means you need to run the service locally using containers (Docker).

Step 1 – Install Docker Engine

Before pulling or running any containers, Docker must be installed on Server1.

Without Docker Engine, no container can be deployed.

Step 2 – Pull the container image

Use docker pull to download the Anomaly Detector container image from Microsoft Container Registry.

This requires internet access, so it should be done when connectivity is available.

Step 3 – Run the container

After pulling the image, use docker run to start the Anomaly Detector container.

This exposes a local REST API endpoint on Server1.

Step 4 – Query the local endpoint

Once the container is running, you can query the prediction endpoint on Server1.

This allows local inference without requiring continuous internet access.

Correct Sequence:

Install the Docker Engine on Server1.

From Server1, run the docker pull command.

From Server1, run the docker run command.

Query the prediction endpoint on Server1.

Use Azure AI Anomaly Detector on containers

Deploy Azure AI services in Docker containers

Anomaly Detector container setup

Microsoft References

Comprehensive Detailed ExplanationRelational databases (such as Azure SQL Database, SQL Server, or Azure Database for PostgreSQL) are designed for structured data and ACID-compliant transactions.

Let’s analyze the options:

Changes to relationships between entities

While relational databases do handle relationships (via primary/foreign keys), this phrase alone does not describe the core usage scenario for “high volume.”

Geographically distributed writes

This is better suited to NoSQL databases (e.g., Azure Cosmos DB) because they are optimized for global distribution and multi-region writes.

Transactional writes

Correct.

Relational databases excel at handling large volumes of transactional operations (INSERT, UPDATE, DELETE) while ensuring consistency, integrity, and rollback capabilities.

This is the primary workload for OLTP (Online Transaction Processing) systems.

Writes that have varying data structures

This is a use case for NoSQL/document databases like MongoDB or Azure Cosmos DB, not relational systems which require structured schema.

Correct Answer: Transactional writes

Azure SQL Database – transactional processing

Data store considerations: relational vs non-relational

OLTP vs OLAP

Microsoft References

Provision the Language service resource in Azure.

Deploy a Docker container to an on-premises server.

Run the container and query the prediction endpoint.

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

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

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

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.

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.

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

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.

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.

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.

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: 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: Azure Blob storage

At the start of the pipeline, you have unstructured text or non-text content (such as images, scanned documents, or JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.

Box 2: Computer Vision API

Scenario: Provide users with the ability to search insight gained from the images, manuals, and videos associated with the products.

The Computer Vision Read API is Azure's latest OCR technology (learn what's new) that extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents.

Box 3: Translator API

Scenario: Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.

Box 4: Azure Files

Scenario: Store all raw insight data that was generated, so the data can be processed later.

Parameter type: stream

Visual features list: VisualFeatureTypes.Description

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

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

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

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

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

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

Microsoft Azure AI Solution References

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

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

To restrict access so that only resources in vnet1 can access the Language service:

You configure the virtual network settings on the Language service resource.

This enables private endpoint integration with the VNet.

NSGs and Azure Firewall apply to subnets/resources but do not configure the service resource itself.

Containers can run Language service locally but are not needed here.

Correct Answer: C

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: api-nam.cognitive.microsofttranslator.com

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

Box 2: /translate

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

You need to detect PPE compliance (masks or safety glasses) on a periodic schedule (every 15 minutes) while minimizing development effort and cost.

Why Computer Vision (Azure AI Vision)Azure AI Vision (formerly “Computer Vision”) lets you analyze still images with prebuilt Image Analysis and—when needed—train a lightweight custom object detection model directly in Vision Studio without building ML pipelines. You can simply grab a frame (snapshot) from your camera feed every 15 minutes and call the Vision API—this keeps costs low because you pay per image analyzed rather than continuously processing video. Microsoft’s current Vision stack supports image analysis and custom object detection via Vision Studio and v4.0 APIs, designed for scenarios like PPE detection. Microsoft Azure+1

Why not FaceThe Face service is intended for face detection/recognition workflows. While Face can return some accessory attributes (e.g., glasses/mask confidence), Microsoft has scaled back several face attributes under Responsible AI guidance, and Face is not positioned as a general PPE-compliance detector across a scene (multiple people, full body PPE). It’s therefore a poorer fit and can increase false decisions for PPE checks beyond the face region. Azure Docs+3

Why not Azure Video Analyzer for Media (Video Indexer)Video Indexer is optimized for extracting insights from full videos (speech, people, labels, brands). Running it just to check PPE every 15 minutes is overkill and more expensive than analyzing a single still frame with Vision. In addition, Microsoft has been adjusting video services post–Azure Media Services retirement; using snapshot + Vision avoids those complexities and keeps costs minimal.

How this meets the requirements

Identify masks/safety glasses: Use Image Analysis or a quick custom object detector in Vision Studio trained on a small set of PPE images. Microsoft Learn

Every 15 minutes: Capture a single frame from the camera and call the Vision endpoint—simple scheduling with a timer-triggered Function or Logic App, minimal code. (General Azure pattern; Vision API is stateless per image.) Microsoft Azure

Minimize development effort: Vision Studio provides no-code/low-code training and testing for custom detectors if the prebuilt model isn’t sufficient. Microsoft Learn

Minimize costs: Pay-per-call image analysis is cheaper than indexing entire video segments; no continuous streaming required. Microsoft Azure

Answer: B. Computer Vision

Microsoft References

Azure AI Vision (product overview): image and video analysis with OCR, object detection, Image Analysis. Microsoft Azure

What’s new in Azure AI Vision: custom image classification/object detection in Vision Studio and v4.0 APIs. Microsoft Learn

Face detection & attributes (accessories including glasses and mask)—not the right tool for scene-level PPE compliance. Microsoft Learn

Face Detect REST API (attributes list, including glasses/mask). Microsoft Learn

Responsible AI updates indicating changes to facial analysis attributes; facial detection/accessories remain but are limited in scope. Azure Docs

Azure AI Video Indexer release notes/updates (video-centric use cases, ongoing adjustments post–AMS retirement).

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

Let’s break it down:

Which Cognitive Service is needed?

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

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

Endpoint selection

Text Analytics uses regional endpoints such as:https:// .api.cognitive.microsoft.com/

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

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

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

API Path selection

To detect language, you call:/text/analytics/v3.1/languages

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

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

Correct Answer Combination:

eastus.api.cognitive.microsoft.com

/text/analytics/v3.1/languages

Text Analytics Language Detection API

Text Analytics REST API v3.1

Microsoft References

The percentage of false positives is [0].

The value for the number of true positives divided by the total number of true positives and false negatives is [100].

The screenshot shows the Performance tab of a Custom Vision object detection model after training.

Two key performance metrics are in focus:

False Positives (FP):

False positives occur when the model incorrectly predicts an object that isn’t present.

In the provided exhibit, the model shows 0% false positives.

This means it did not incorrectly classify any non-object as an object.

Recall (True Positives ÷ (True Positives + False Negatives)):

This measures how many of the actual objects were correctly detected.

Formula: Recall = TP / (TP + FN).

In the performance report, recall is 100%, meaning the model correctly detected all the actual objects (no false negatives).

False positives: 0%

Recall (TP ÷ (TP + FN)): 100%

Evaluating the answer choices:

Correct Answers:

False positives → 0

Recall → 100

Custom Vision performance metrics

Precision, recall, and accuracy in machine learning

Microsoft References

In SQL, commands are categorized into different language groups depending on their purpose:

Data Manipulation Language (DML):

Deals with manipulating the data stored in existing database objects.

Includes:

SELECT → query data

INSERT → add new records

UPDATE → modify existing records

DELETE → remove records

Therefore, UPDATE is a DML statement.

Data Definition Language (DDL):

Used to define and manage database schema objects (tables, indexes, schemas).

Includes: CREATE, ALTER, DROP, TRUNCATE.

Data Control Language (DCL):

Used to control access and permissions.

Includes: GRANT, REVOKE.

Now let’s map the options:

A. REVOKE → DCL (not DML).

B. UPDATE → DML ✅ (correct).

C. DROP → DDL (removes database objects).

D. CREATE → DDL (creates database objects).

Correct Answer: B. UPDATE

SQL Language Elements – DDL, DML, DCL

UPDATE (Transact-SQL)

Microsoft References

The system message you provided—“You are an AI assistant that helps people solve mathematical puzzles. Explain your answers as if the request is by a 4-year-old.”—is classic priming (also called system prompt/role prompting). Priming sets the assistant’s persona, objectives, tone, and constraints before any user message, steering all subsequent responses accordingly.

Why the other options are incorrect:

A. Few-shot learning – supplies example Q&A pairs in the prompt to teach the model by example; none were included here.

B. Affordance – relates to interface cues that guide users; not a prompt technique for model behavior.

C. Chain of thought – asks the model to show intermediate reasoning steps; your prompt doesn’t request step-by-step rationale, only a style/tone.

Microsoft Azure AI References

Azure OpenAI – Prompt engineering techniques (role/system prompts, priming).

Azure OpenAI – System message (role) guidance for controlling assistant behavior and tone.

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

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

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

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

Both must map to the same answer.

Problem

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

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

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

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

Retrain the model.

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

This step incorporates the updates into the QnA Maker service.

Republish the model.

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

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

Correct Sequence of Actions

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

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

Actions That Are Not Required

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

Retrain the model.

Republish the model.

Final Answer (Sequence)

QnA Maker – Add alternative phrasing

Train and publish a QnA Maker knowledge base

Microsoft References