Microsoft AI-102 Designing and Implementing a Microsoft Azure AI Solution Exam Practice Test

When using Azure Translator REST API, required headers are:

Ocp-Apim-Subscription-Key → subscription key

Ocp-Apim-Subscription-Region → resource region (if using a multi-service Cognitive Services resource)

Content-Type → typically application/json or application/xml

Other options:

Client trace ID is optional for debugging.

Resource ID, access control request, content language, content length are not required headers.

Correct Answer: B

You need to minimize indexing time for 10 GB of scanned docs and images.

Azure Cognitive Search supports parallel indexing to increase throughput by partitioning work.

A text-based indexer or scheduled indexing will not reduce build time.

Field mappings help transform fields but don’t affect indexing speed.

Therefore, the correct way to speed up index building is parallel indexing.

Correct Answer: A

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"

To access multiple Azure AI capabilities (e.g., Speech and Language) using a single endpoint and a single set of credentials, you should provision an Azure AI multi-service resource (called Azure AI service in the portal; formerly “Cognitive Services” multi-service account). This resource provides one endpoint and one key (or Microsoft Entra ID) that authorizes calls across supported services, including Speech and Language. Creating separate “Azure AI Speech” or “Azure AI Language” resources would give you distinct endpoints/keys for each, which does not meet the requirement. Azure AI Content Safety is unrelated.

Microsoft References

Azure AI services multi-service resource (single endpoint/credential across services).

What are Azure AI services (overview of consolidated access model).

Scenario Recap:

Application: Flower classification.

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

Analysis:

This is the best practice workflow:

Add new labeled training images.

Retrain the model with updated dataset.

Publish the retrained model.

This clearly meets the goal.

Correct Answer: A. Yes

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

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

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

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

References (Microsoft Docs):

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

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

Azure AI Content Safety supports custom blocklists—dictionaries of terms/phrases you define to detect obscure or domain-specific offensive terms in user content. Creating a blocklist is the minimal-effort, built-in way to meet this requirement; you don’t need to build a classifier.

References

Create and use blocklists in Azure AI Content Safety. arn.microsoft.com/azure/ai-services/content-safety/concepts/blocklists

Text moderation overview (using built-in classifiers with optional blocklists).

To detect harmful content with Azure AI Content Safety using the .NET SDK, you instantiate a ContentSafetyClient with the endpoint and key. For text moderation, you construct an AnalyzeTextOptions object with the input string (and optionally specify categories, blocklists, etc.). You then call client.AnalyzeText(AnalyzeTextOptions options) and inspect the result (e.g., categories analysis and blocklist matches).

Code completed:

var client = new ContentSafetyClient(new Uri(endpoint), new AzureKeyCredential(key));

var request = new AnalyzeTextOptions("what is the weather forecast for Seattle");

Response response = client.AnalyzeText(request);

Microsoft Azure AI References

Azure AI Content Safety – SDK usage for text analysis with ContentSafetyClient and AnalyzeTextOptions (Analyze Text API).

Azure AI Content Safety – Concepts: text categories (hate/sexual/violence/self-harm) and blocklists, returned in AnalyzeTextResult.

When working with Speech Studio in Azure AI Speech (for example, to build a custom speech model for recognition or training purposes), you must provide speech samples along with their transcripts.

Audio File Format

The supported format is .wav (PCM, 16-bit, 16 kHz or 8 kHz mono).

Other formats like .wma, .flac, or .mp3 are not accepted for training custom models.

Batch Upload via .zip

Audio files must be placed in a .zip archive.

The .zip file must also include a transcription file (usually a .txt or .json file) that maps the spoken audio in each .wav file to the correct text.

Transcript Format

Transcripts must be plain text, not Microsoft Word documents.

The transcript must align correctly with each audio file to ensure the Speech service learns from accurate mappings.

Requirements for Uploading Speech Samples:

A. Combine into one .wma file

Incorrect. .wma is not supported. Also, Speech Studio expects multiple .wav samples, not one big file.

B. Upload a .zip file containing .wav files + transcript

Correct. This is the officially supported method for uploading training data into a Speech Studio project.

C. Upload .flac files with Word transcript

Incorrect. .flac is not supported. Transcripts must be plain text, not Word files.

D. Upload individual .wma files

Incorrect. .wma is not supported.

Option Analysis

Correct Answer: B. Upload a .zip file that contains a collection of audio files in the .wav format and a corresponding text transcript file.

Prepare training data for Custom Speech

Speech Studio – Data Requirements

Microsoft References

The Computer Vision API uses text recognition APIs to extract and recognize text information from images. Read uses the latest recognition models, and is optimized for large, text-heavy documents and noisy images.

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

For a social media messaging app that needs to determine the language of short text in real time, the most appropriate service is Azure AI Translator. Translator provides a low-latency language detection operation for text, returning the detected language code and a confidence score, and it’s optimized for interactive scenarios like chat and messaging. You can use detection on its own or feed the result directly into translation if needed.

Why not the others:

Azure AI Speech is for audio (speech-to-text, text-to-speech, speech translation), not text-only language detection.

Azure AI Language has language detection within its Text Analytics features, but Translator’s detect endpoint is the recommended, streamlined choice for real-time translation pipelines and chat apps.

Azure AI Foundry Content Safety is for moderation (toxicity, harassment, hate), not language identification.

Microsoft References

Azure AI Translator — Text Translation overview (includes language detection).

Text Translation REST API v3.0 — Detect operation.

Quickstart: Use the Translator text client libraries (shows detect + translate flow).

Scenario RecapYou are building a remote training monitoring solution.

Requirement: Use video and audio feeds to detect if a learner is present, paying attention, and talking.

Services available: Face, Speech, Text Analytics.

From a learner’s video feed, verify whether the learner is present.

The Face API can detect and identify faces in a video feed.

It can tell if a person is present and recognized, fulfilling the requirement.

From a learner’s facial expression in the video feed, verify whether the learner is paying attention.

Again, the Face API provides facial expression and emotion recognition (happiness, anger, neutral, etc.).

This can be mapped to “paying attention vs. distracted.”

From a learner’s audio feed, detect whether the learner is talking.

The Speech service detects spoken input and can determine if speech is present.

Text Analytics works on text (not raw audio) and is therefore not appropriate here.

Analysis

From a learner’s video feed, verify whether the learner is present: Face

From a learner’s facial expression in the video feed, verify whether the learner is paying attention: Face

From a learner’s audio feed, detect whether the learner is talking: Speech

Final Answer (Answer Area Selections)

Face API – Face detection & identification

Face API – Emotion recognition

Azure Speech service

Microsoft References

To identify city names with Azure AI Language, you use the Text Analytics client and its Named Entity Recognition (NER) capability. In the .NET SDK, create a TextAnalyticsClient and call RecognizeEntities(...). This returns a CategorizedEntityCollection, where each CategorizedEntity has properties such as Text, Category, SubCategory, and ConfidenceScore.

City mentions are recognized under the broader Location category, and the more specific type is surfaced in SubCategory (commonly "City"). Therefore, iterating the returned entities and filtering with entity.SubCategory.Contains("City") correctly locates city names. Using DocumentAnalysisClient or FormRecognizerClient would be incorrect because those are for document/form extraction scenarios, not general-language NER. QuestionAnsweringClient is for Q&A over knowledge sources. Hence the correct client is TextAnalyticsClient, and the correct discriminator is SubCategory.

Microsoft Azure AI References (titles only)

Azure AI Language – Text Analytics client library: Recognize Entities

Azure AI Language – Named Entity Recognition concepts and categories

Azure AI Language – CategorizedEntity model (Category, SubCategory, ConfidenceScore)

 The subscription will be charged 86 tokens for the execution of the session. No

 The text completion was truncated because the Max response tokens value was exceeded. No

 The prompt_tokens value will be included in the calculation of the Max response tokens value. No

From the response payload:

usage.prompt_tokens = 37

usage.completion_tokens = 86

usage.total_tokens = 123

finish_reason = "stop"

Billing is for total tokens (input + output), not just completion tokens.Azure OpenAI bills on both prompt (input) and completion (output) tokens. Here, the total is 123 tokens, so it is not just 86. Hence No.

Not truncated by max response tokens.If a response hits the maximum allowed output tokens, finish_reason would be "length". Here it is "stop" and the model produced only 86 completion tokens while Max response tokens was set to 100, so the limit was not reached. Hence No.

max response tokens applies only to the generated output, not the prompt.max_tokens (Max response tokens) limits the number of tokens the model may generate in the completion; it does not include prompt_tokens. Hence No.

Microsoft References

Azure OpenAI Service – Chat Completions API (parameters, max_tokens, response usage, finish_reason).

Azure OpenAI pricing and token usage model (charges for input and output tokens).

Token counting and limits (context vs. max output tokens).

Comprehensive Detailed ExplanationYou trained an object detection model obj1 inside project proj1 on the acvdev Custom Vision resource and need to move it to the acvprod resource.

Moving a model between Custom Vision resources is done by exporting the project (not an iteration) from the source resource and importing it into the target resource.

Step 1: GetProjects (acvdev)Retrieve the list of projects on the development resource to obtain the project ID of proj1. The export operation requires the project ID.

Step 2: ExportProject (acvdev)Call the Export Project API on the development resource to generate the portable project package (includes tags, images’ metadata, iterations, settings). This is specifically intended for moving/cloning a project across resources or regions.

Step 3: ImportProject (acvprod)Use the Import Project API on the production resource to create a new project from the exported package. This recreates the project (and associated iterations/metadata) under acvprod.

Notes:

ExportIteration is for exporting a trained iteration to formats for edge/container or mobile use, not for transferring to another Custom Vision resource.

UpdateProject is for changing metadata (name, description, domain) of an existing project and is not part of the move workflow.

Custom Vision REST API – Projects: Export/Import

Custom Vision REST API – Projects API (Get projects)

Custom Vision – Export model vs. Export project guidance (describes iteration export purpose versus project portability)

References (Microsoft Azure AI Solution)

Comprehensive Detailed ExplanationThe method shown uses the Read API of the Azure Computer Vision client library. The ReadAsync call submits the OCR job and returns an Operation-Location header, which contains the operationId. Then GetReadResultAsync(Guid.Parse(operationId)) is called to fetch the result.

The Read API is asynchronous.

Submitting the read request does not mean the OCR has finished.

If you immediately call GetReadResultAsync, the operation may still be in progress, resulting in incomplete or empty results.

Check the results.Status property

The response from GetReadResultAsync includes a status property.

Possible values: notStarted, running, succeeded, failed.

You must wait until the status is succeeded before processing results.

This ensures you don’t attempt to read lines before OCR has completed.

This corresponds to B.

Wrap in a loop with delay

Since OCR takes time, you need to poll the service until the status becomes succeeded or failed.

A typical pattern is:

ReadOperationResult results;

do

{

results = await client.GetReadResultAsync(Guid.Parse(operationId));

await Task.Delay(1000); // wait 1 second before retrying

}

while (results.Status == OperationStatusCodes.Running || results.Status == OperationStatusCodes.NotStarted);

This corresponds to D.

The IssueRequired Fixes

A. Remove the Guid.Parse(operationId) parameter

Incorrect. The operationId is required to fetch the OCR results.

B. Add code to verify the results.Status value

Correct. You must check whether the operation has completed.

C. Add code to verify the status of the txtHeaders.Status value

Incorrect. txtHeaders only contains metadata and the operation location. The OCR completion status comes from the results.Status in the GetReadResultAsync response, not from txtHeaders.

D. Wrap the call to GetReadResultAsync within a loop that contains a delay

Correct. This is how you poll until the operation has finished.

Option Analysis

Correct Answer: B and D

Azure Computer Vision Read API

Read text using the Computer Vision client library

Microsoft References

Comprehensive Detailed ExplanationYou need to analyze confidential documents with the Language service while ensuring those documents never leave your premises. The supported pattern for Azure AI Services (Cognitive Services) is to run the Language service container on-premises and send the app’s requests to that local container endpoint. The container performs inference locally; only metering/billing calls are sent to Azure.

To achieve this:

Provision an on-premises Kubernetes cluster that has internet connectivity.Azure AI service containers running on-prem require outbound access to Azure for metering and billing unless you’re using the special disconnected model (not implied here). Therefore, the cluster must have internet connectivity so the container can reach the Azure billing endpoint while keeping document content local.

Pull an image from the Microsoft Container Registry (MCR).Official Azure AI service containers (including Language) are published in MCR, not Docker Hub. You must pull the Language (Text Analytics/Language) container image from MCR.

Run the container and specify an API key and the Endpoint URL of the Cognitive Services resource.When you start the container, you configure it with the API key and billing (endpoint) URL of your Azure Cognitive Services (multi-service or Language) resource. This enables the container to authenticate and report usage to Azure, while your data stays on-prem.

Why not the other options?

AKS would place workloads in Azure, which isn’t required when the requirement is to keep documents on-prem.

On-prem cluster isolated from the internet would block the container’s required calls to Azure’s billing endpoint.

Pull from Docker Hub is incorrect because Azure AI service containers are published to MCR.

App ID and Client Secret are not the standard parameters for Azure AI service containers; they require API key + billing endpoint.

Use Azure AI services in containers (billing/endpoint and API key requirements, data remains local):Microsoft Learn – Install and run containers for Azure AI services and Use containers with Azure AI services (metering and billing).

Language service container images on Microsoft Container Registry (MCR):Microsoft Learn – Deploy language containers (Text Analytics/Language).

Networking requirements (internet connectivity for metering):Microsoft Learn – Configure containers and network access for Azure AI services containers.

Microsoft Azure AI Solution ReferencesThese documents collectively show that: (a) containers are pulled from MCR, (b) they are configured with API key + endpoint for billing, and (c) internet egress is required for metering while payload data remains on-premises.

Explanation:

By default, Azure AI Language may log request and response data for monitoring.

Setting the loggingOptOut parameter to true ensures that input data is not persisted after analysis.

piiCategories specifies which PII types to detect.

showStats enables request/response statistics.

model-version specifies which model version to use.

Correct Answer: A

For an Azure AI Language Custom Question Answering project (QA1), you can import Q&A pairs directly from Excel (.xls/.xlsx) or TSV files in Language Studio. Microsoft’s guidance explicitly states that when importing question–answer sources into a project, you can choose Excel or TSV. JSON, LU, and CSV are not supported as import file types for Q&A pairs in Language Studio. Microsoft Learn

Additional Microsoft guidance notes that structured TXT, TSV, and XLS files can be uploaded to create or augment a project, and clarifies import/export behavior for TSV and XLS. This reinforces that Excel and TSV are the supported formats here. Microsoft Learn

Microsoft References:

Move projects and Q&A pairs – “Import question and answers… option to import either an Excel or TSV file.” Microsoft Learn

Format guidelines for Custom Question Answering – structured TXT, TSV, and XLS supported; import/export notes for TSV and XLS. Microsoft Learn

To index millions of PDFs/images quickly:

Partition the blob data into virtual folders (prefixes) and create multiple indexers, one per folder, so they can run in parallel.

Increase search units (SUs) to raise indexing throughput.

Schedule the indexers with the same cadence so they run concurrently.Creating multiple services (A, C) is unnecessary and costly. Scheduling sequential execution (B) defeats the goal of “as quickly as possible.”

References

Azure Cognitive Search indexer performance guidance: parallelizing blob indexing using multiple indexers over virtual folders/prefixes and scaling SUs for higher throughput.

Upload to: An Azure Storage account

Model type: Custom neural

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

Step 1: Upload location

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

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

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

Step 2: Model type

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

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

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

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

Thus, the correct configuration is:

Upload to Azure Storage account

Use Custom neural model

Correct Answer:

Upload to: An Azure Storage account

Model type: Custom neural

Azure AI Document Intelligence – Custom models

Custom neural vs custom template models

Train a custom model with Azure Storage input

Microsoft References

To analyze receipts from a URL, use the StartRecognizeReceiptsFromUri method

Example code:

private static async Task AnalyzeReceipt(

FormRecognizerClient recognizerClient, string receiptUri)

{

RecognizedFormCollection receipts = await recognizerClient.StartRecognizeReceiptsFromUri(new Uri(receiptUrl)).WaitForCompletionAsync();

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

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

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

What is translated?

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

LU reference link texts

List of .lu files under a specific path.

According to the Microsoft documentation, Computer Vision is a cloud-based service that provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices.

According to the Microsoft documentation, image type detection is one of the features of Computer Vision that can categorize an image as either a photograph or a drawing. You can use the image type detection feature by calling the Analyze Image API with the visualFeatures parameter set to ImageType. The API will return a JSON response with an imageType field that indicates whether the image is a photo or a clipart.

According to the Microsoft documentation, image descriptions is another feature of Computer Vision that can generate a caption for an image. You can use the image descriptions feature by calling the Analyze Image API with the visualFeatures parameter set to Description. The API will return a JSON response with a description field that contains a list of captions for the image, each with a confidence score.

Therefore, by using these two features of Computer Vision, you can achieve your app requirements with minimal development effort. You don’t need to use any other services, such as object detection, content tags, or Custom Vision, which are designed for different purposes.

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

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.

Azure Video Indexer can ingest a video by URL as long as the URL is publicly accessible and directly downloadable (no authentication or HTML landing page). Creating a download link in OneDrive produces a direct-file URL that Video Indexer can fetch and index without you re-uploading the 20-GB file through your browser. A generic OneDrive “sharing link” often lands on a web page (not a direct file) and may require authentication, which Video Indexer cannot crawl. Uploading locally is possible but unnecessary and time-consuming for a 20-GB video.

Microsoft References

Upload/index videos in Video Indexer (supports uploading from a public URL).

Video Indexer quotas and limits (file size limits; uploading by URL).

Analyze lesson plans and extract key fieldsUse Azure AI Document Intelligence (Form Recognizer) to automatically extract structured data (dates/times, required texts, names, etc.) from uploaded lesson-plan documents (PDFs/images). It provides prebuilt and custom extraction with minimal code via Document Intelligence Studio.

Analyze learning content and show pictures for common words/phrasesUse Immersive Reader, which includes features like Picture Dictionary and text comprehension supports directly in apps with minimal development. It can highlight words and show illustrative images to aid vocabulary learning—exactly the requirement.

Other options are not a fit:

Azure AI Custom Vision is for training image classifiers/detectors, not text comprehension.

Azure AI Search indexes and searches content; it doesn’t provide picture dictionary/reading experience.

Document Intelligence (Form Recognizer) overview & extraction capabilities: learn.microsoft.com/azure/ai-services/document-intelligence/overview

Immersive Reader – Picture Dictionary & reading features: learn.microsoft.com/azure/ai-services/immersive-reader/overview

Microsoft Azure AI References

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

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

Precision:

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

Formula: Precision = TP / (TP + FP)

Helps determine how reliable positive predictions are.

Recall:

The percentage of actual positives that were correctly predicted.

Formula: Recall = TP / (TP + FN)

Helps determine how many of the true positives were captured.

Metrics Provided by Custom Vision:These two metrics are explicitly shown in the Custom Vision portal and via API.

A. Recall

Yes, Custom Vision reports recall.

Correct

B. F-score

Not directly reported in the Custom Vision portal.

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

Incorrect

C. Weighted accuracy

Not reported by Custom Vision.

Incorrect

D. Precision

Yes, Custom Vision reports precision.

Correct

E. Area under the curve (AUC)

Not reported by Custom Vision.

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

Incorrect

Option Analysis

Correct Answer: A. recall, D. precision

Evaluate the prediction performance of your classifier in Custom Vision

Custom Vision training and evaluation

You need to identify industry-specific technical terms in documents. That is a classic use case for Custom Named Entity Recognition (custom NER), which lets you build a model to extract domain-specific entities (for example, chemical names, part numbers, contract IDs) from unstructured text. Among the options, only custom NER is designed to recognize entities unique to your domain.

Why not Key Phrase Extraction? It returns the main concepts/talking points of text, not typed, domain-specific entities.

Why not CLU? Conversational Language Understanding is for intent and entity extraction from user utterances in conversations, not for mining technical terms from documents. Microsoft Learn

Why not Language Detection? It detects the language of the text only.

Custom NER fits: Purpose-built to extract domain-specific entities with minimal code; you label a small dataset and train in Language Studio.

Microsoft References:

Custom Named Entity Recognition overview – purpose is extracting domain-specific entities. Microsoft Learn

Transparency note for custom NER – emphasizes domain-specific extraction. Microsoft Learn

Key Phrase Extraction overview – identifies main concepts, not domain entities. Microsoft Learn

CLU overview – intent/entity for conversational scenarios. Microsoft Learn

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

Defender for Cloud Apps (A): Focused on monitoring SaaS app usage and cloud security, not image content moderation → ❌

Custom Vision (B): Can classify images, but requires building/training your own model → more development effort → ❌

Azure AI Vision (C): Can analyze images, including adult/racy content detection, but Content Safety is now the preferred service for moderation. Still valid → ✅

Azure AI Content Safety (D): Specifically designed for detecting and blocking harmful/inappropriate content in text and images → ✅

Document Intelligence (E): Extracts text/structure from documents, not relevant here → ❌

Correct Answer:

C. Azure AI Vision and D. Azure AI Content Safety

Removing all the chit-chat question and answer pairs from the project will not ensure that the chatbot provides formal responses to spurious questions. It will only make the chatbot unable to handle any chit-chat scenarios, which may result in a poor user experience and a loss of engagement. Instead, you should choose a chit-chat personality that matches the tone and style of your chatbot, such as Professional or Caring. You can also edit the chit-chat questions and answers to suit your specific needs, or add new ones that are not in the predefined data set12. This way, you can ensure that the chatbot responds appropriately to spurious questions, while still maintaining a conversational and engaging interaction with the user.

For Azure OpenAI fine-tuning, training data must be provided as a JSON Lines (.jsonl) file. Each line represents one example. Because your data is described as prompt–completion pairs, the required shape is typically:

{"prompt": "", "completion": ""}

(Chat model fine-tuning instead uses a messages array per line, but the file format is still JSONL.) JSONL is the only accepted format for upload to the fine-tuning job.

Microsoft References

Azure OpenAI Service – Fine-tuning: data preparation and formats (JSONL; prompt–completion and chat messages).

Azure OpenAI Service – How to fine-tune models (training files must be .jsonl).

In the Bot Framework SDK, if you want to collect structured input step by step, you typically use a Waterfall dialog. Each step in the waterfall represents one piece of logic, such as asking for the size, toppings, or type of food item.

Prompts are used inside waterfall steps to actually ask the user for input (e.g., text prompt, choice prompt, date-time prompt).

Adaptive, Action, Input are concepts more relevant to Power Virtual Agents or Adaptive Dialogs (Composer), not the Bot Framework SDK question context.

Since the question specifically mentions Bot Framework SDK, the correct answer is Waterfall dialog + Prompts.

Correct Answer: C, D

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

Box 1: Azure Blob storage

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

Box 2: Computer Vision API

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

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

Box 3: Translator API

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

Box 4: Azure Files

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

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

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.

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: 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

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

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.

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.

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

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.

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.

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.

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

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.