Microsoft Practice Questions, Discussions & Exam Topics by our Authors
SNAPSHOT -
You are developing an application that will use the Computer Vision client library. The application has the following code.
For each of the following statements, select Yes if the statement is...
Author: Oliver · Last updated Jul 19, 2026
You are developing a method that uses the Computer Vision client library. The method will perform optical character recognition (OCR) in images. The method has the following code.
During testing, you discover that the call to the GetReadResultAsync method occurs before the read operation is complete.
You need to prevent the GetReadResultAsync method from proceeding until the re...
Author: Ethan · Last updated Jul 19, 2026
SNAPSHOT -
You have a Computer Vision resource named contoso1 that is hosted in the West US Azure region.
You need to use contoso1 to make a different size of a product photo by using the smart cropping feature.
How should you complete the API URL? To answer...
Author: Isabella1 · Last updated Jul 19, 2026
DRAG DROP -
You are developing a webpage that will use the Azure Video Analyzer for Media (previously Video Indexer) service to display videos of internal company meetings.
You embed the Player widget and the Cognitive Insights widget into the page.
You need to configure the widgets to meet the following requirements:
* Ensure that users can search for keywords.
* Display the names and faces of people in the video.
* Show captions in the video in English (United States).
How should you complete the URL for each widget? To answer, drag the appro...
Author: Evelyn · Last updated Jul 19, 2026
DRAG DROP -
You train a Custom Vision model to identify a company's products by using the Retail domain.
You plan to deploy the model as part of an app for Android phones.
You need to prepare the model for deployment.
Which three actions should you perform in sequence? To answer, move ...
Author: Nathan · Last updated Jul 19, 2026
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You are developing an application to recognize employees' faces by using the Face Recognition API. Images of the faces will be accessible from a URI endpoint.
The application has the following code.
For each of the following statements, select ...
Author: Zara · Last updated Jul 19, 2026
DRAG DROP -
You have a Custom Vision resource named acvdev in a development environment.
You have a Custom Vision resource named acvprod in a production environment.
In acvdev, you build an object detection model named obj1 in a project named proj1.
You need to move obj1 to acvprod.
Which three actions should you perform in sequen...
Author: Rohan · Last updated Jul 19, 2026
DRAG DROP -
You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.
You need to use the Custom Vision API to help detect common faults.
Which three actions should you perform in sequence? To answer,...
Author: Siddharth · Last updated Jul 19, 2026
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You are building a model that will be used in an iOS app.
You have images of cats and dogs. Each image contains either a cat or a dog.
You need to use the Custom Vision service to detect whether the images is of a cat or a dog.
How should you configure the project in the Custom Vision ...
Author: Abigail · Last updated Jul 19, 2026
You have an Azure Video Analyzer for Media (previously Video Indexer) service that is used to provide a search interface over company videos on your company's website.
You need to be abl...
In the scenario where you need to search for videos based on who is present in the video, the best option is to use A) Create a person model and associate the model to the videos.
Reasoning for selecting Option A:
Azure Video Analyzer for Media provides features like facial recognition, where you can create a custom person model and associate it with specific videos. This allows the system to recognize and index individuals across multiple videos. By creating and associating a person model with videos, you enable the search functionality based on the individuals present, making it a seamless way to search for videos with specific people.
Rejection of other options:
- B) Create person objects and provide face images for each object: While this could be useful in some cases, it’s less efficient than creating a person model for the entire video dataset. This option may involve more manual work and doesn't scale as easily as associating a model to the videos for aut...
Author: NebulaEagle11 · Last updated Jul 19, 2026
You use the Custom Vision service to build a classifier.
After training is complete, you need to evaluate the classifier.
Which two metrics are available for review? Each correct answe...
When evaluating a classifier in Azure's Custom Vision service, the most common and relevant metrics to review are A) recall and D) precision.
Reasoning for selecting Option A (Recall) and Option D (Precision):
- A) Recall: Recall is a metric that measures the ability of the classifier to correctly identify positive instances. It’s particularly useful in scenarios where it is important not to miss any positive class (e.g., detecting rare diseases in medical imaging). Custom Vision provides recall as one of the evaluation metrics because it helps you understand how well your model identifies the target class among all actual positives.
- D) Precision: Precision measures the accuracy of the positive predictions made by the classifier. In other words, it answers the question, "Of all the instances the model predicted as positive, how many were actually positive?" Precision is particularly important when the cost of false positives is high (e.g., detecting fraud in financial transactions). Custom Vision includes precision as a key metric for evaluating how reliable the classifier is in making correct positive predictions.
Rejection of other options:
- B) F-score: While the F-score (or F1-score) combines precision and recall into a single metric, Azure Custom Vision service typically reports precision and recall directly as separ...
Author: Liam · Last updated Jul 19, 2026
DRAG DROP -
You are developing a call to the Face API. The call must find similar faces from an existing list named employeefaces. The employeefaces list contains 60,000 images.
How should you complete the body of the HTTP request? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not a...
Author: Emma Brown · Last updated Jul 19, 2026
DRAG DROP -
You are developing a photo application that will find photos of a person based on a sample image by using the Face API.
You need to create a POST request to find the photos.
How should you complete the request? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at a...
Author: Maya · Last updated Jul 19, 2026
DRAG DROP -
You are developing a photo application that will find photos of a person based on a sample image by using the Face API.
You need to create a POST request to find the photos.
How should you complete the request? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at a...
Author: Olivia Johnson · Last updated Jul 19, 2026
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You develop an application that uses the Face API.
You need to add multiple images to a person group.
How should you complete the code? To answer, select the appropriate option...
Author: Kai99 · Last updated Jul 19, 2026
Your company uses an Azure Cognitive Services solution to detect faces in uploaded images. The method to detect the faces uses the following code.
You discover that the solution frequently fails to detect faces in blurred images and in images that contain sideways faces.
You need to increase ...
Author: RadiantPhoenixX · Last updated Jul 19, 2026
You have the following Python function for creating Azure Cognitive Services resources programmatically. def create_resource (resource_name, kind, account_tier, location) : parameters = CognitiveServicesAccount(sku=Sku(name=account_tier), kind=kind, location=location, properties={}) result = client.accounts.create(resource_group_name, resource_name, parameters)
You need to call the fu...
In this scenario, the goal is to create a free Azure resource in the West US region that will be used to generate captions for images. The correct option is A) create_resource("res1", "ComputerVision", "F0", "westus").
Reasoning for selecting Option A:
- "ComputerVision" is the correct service for generating captions for images automatically. It provides a set of tools for analyzing and understanding images, including automatic captioning. This aligns with the requirement to create a service for image captioning.
- "F0" refers to the free tier for the service. In Azure Cognitive Services, the "F0" SKU is the free tier, which fits the requirement of creating a free resource.
- "westus" is the correct region as specified in the prompt.
Rejection of other options:
- B) create_resource("res1", "CustomVision.Prediction", "F0", "westus"): "CustomVision.Prediction" is used for predictions related to custom models created using the Custom Vis...
Author: Julian · Last updated Jul 19, 2026
You are developing a method that uses the Computer Vision client library. The method will perform optical character recognition (OCR) in images. The method has the following code.
During testing, you discover that the call to the GetReadResultAsync method occurs before the read operation is complete.
You need to prevent the GetReadResultAsync method from proceeding until the re...
Author: StarryEagle42 · Last updated Jul 19, 2026
SNAPSHOT -
You are building an app that will enable users to upload images. The solution must meet the following requirements:
* Automatically suggest alt text for the images.
* Detect inappropriate images and block them.
* Minimize development effort.
You need to recommend a computer vision endpoint for each requirement.
What ...
Author: Chloe · Last updated Jul 19, 2026
You need to build a solution that will use optical character recognition (OCR) to scan sensitive documents by using the Computer Vision API. The soluti...
When building a solution that uses OCR to scan sensitive documents and cannot be deployed to the public cloud, there are specific factors that must be considered. These include security (ensuring sensitive data is kept within your organization's premises), the need to use the Computer Vision API, and not relying on public cloud deployment.
Evaluation of the Options:
A) Build an on-premises web app to query the Computer Vision endpoint.
- Rejection Reason: This option suggests using an on-premises web app, but still requires querying the Computer Vision endpoint, which is hosted in the public cloud. Since the requirement is to avoid the public cloud, this option does not satisfy the constraint.
- Scenarios: Could be used when interacting with a public cloud service from a local app, but does not meet the "not deployed to the public cloud" criterion.
B) Host the Computer Vision endpoint in a container on an on-premises server.
- Selection Reason: This option allows you to host a containerized version of the Computer Vision API on-premises, meaning the entire process will stay within your organization’s private infrastructure. This ensures that sensitive data doesn't leave the premises and the service is still leveraging the functionality of the Computer Vision API.
- Scenarios: Ideal when a private, on-premises solution is necessary for privacy or regulatory reasons, while still needing the capabilities of a powerful API like the Computer Vision API.
C) Host an exported Open Neural Networ...
Author: VioletCheetah55 · Last updated Jul 19, 2026
You have an Azure Cognitive Search solution and a collection of handwritten letters stored as JPEG files.
You plan to index the collection. The solution must ensure that queries can be performed on the contents of the l...
When creating an indexer for a collection of handwritten letters stored as JPEG files in Azure Cognitive Search, the goal is to make the content of the letters searchable. The solution requires a skillset that processes the images to extract the text content. Let’s evaluate each option:
Evaluation of the Options:
A) Image analysis
- Rejection Reason: Image analysis focuses on extracting metadata from images, such as identifying objects, faces, or labels. It is not specifically designed to extract text from images, especially handwritten text. This skill would not directly help in indexing the contents of the letters, as it does not extract readable text.
- Scenarios: This skill could be used when you need to analyze images for non-textual content, such as image recognition or object detection, but it is not suitable for extracting text.
B) Optical Character Recognition (OCR)
- Selection Reason: OCR is designed specifically to extract text from images, including handwritten text. Since your collection consists of JPEG files with handwritten letters, OCR will process these images and extract the text, making it possible to index the contents of the letters. The OCR skill is the most appropriate here because it is optimized for text extraction from both printed and handwritten content in images.
- Scenarios: OCR is ideal for processing handwritten or printed text from images, which directly addresses the need to index the content of the h...
Author: StarryEagle42 · Last updated Jul 19, 2026
SNAPSHOT
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You have a library that contains thousands of images.
You need to tag the images as photographs, drawings, or clipart.
Which service endpoint and response property should you use? To answer, select ...
Author: Aria · Last updated Jul 19, 2026
You have an app that captures live video of exam candidates.
You need to use the Face service to validate that the subject...
When you are trying to validate that subjects in a video are real people, the primary concern is determining whether the subjects are human and detecting changes or inconsistencies that might indicate whether the video is of a real person or a static image (e.g., photos or videos of fake people). The Face service in Azure provides tools for detecting and analyzing faces, but we need to focus on the options that allow us to identify signs of a real, live person.
Evaluation of the Options:
A) Call the face detection API and retrieve the face rectangle by using the FaceRectangle attribute.
- Rejection Reason: While the FaceRectangle attribute gives the position of the face in an image, it does not provide any information about whether the subject is a real, live person. It simply locates the face but doesn’t address the concern of live verification. This option is useful for face detection, but not for validating that the subject is a real person.
- Scenarios: This option is useful when you need to detect the face’s location in a frame, but not when you need to check if the subject is live.
B) Call the face detection API repeatedly and check for changes to the FaceAttributes.HeadPose attribute.
- Selection Reason: The HeadPose attribute provides information about the orientation of the subject’s head (e.g., pitch, roll, yaw). By repeatedly calling the Face service during a live video, you can track if the head is moving, which is a strong indicator of a real, live person. If the head remains static, it might suggest the subject is not real, such as in the case of a static image or a fake video. This option can be used effectively for validating whether the subject is a real person.
- Scenarios: This is the...
Author: Ravi Patel · Last updated Jul 19, 2026
SNAPSHOT
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You make an API request and receive the results shown in the following exhibits.
Use the drop-down menus to select the answer choice that completes each statement based on the i...
Author: Victoria · Last updated Jul 19, 2026
You have an Azure subscription that contains an AI enrichment pipeline in Azure Cognitive Search and an Azure Storage account that has 10 GB of scanned documents and images.
You need to index the documents and images in the stor...
To index the documents and images in your Azure Storage account with minimal time, let’s break down the options and consider the key factors for selecting the most suitable approach.
Option A: From the Azure portal, configure parallel indexing.
- Reasoning: Parallel indexing is an option that enables the indexer to split the workload across multiple machines, processing multiple documents at once. This can significantly reduce the time it takes to build the index, especially when dealing with large datasets like the 10 GB of documents and images in your storage account.
- Key Factor: The primary benefit is the reduction of indexing time by leveraging parallelism. This makes it the best fit for a scenario where you want to minimize the time it takes to build the index.
- Scenario: Best used when there is a large volume of data that needs to be processed quickly.
Option B: From the Azure portal, configure scheduled indexing.
- Reasoning: Scheduled indexing allows you to run indexing jobs at specific intervals, which is more suited for ongoing data updates rather than minimizing the initial indexing time.
- Key Factor: It does not directly minimize indexing time for a one-time bulk process; instead, it focuses on scheduling the indexer...
Author: Evelyn · Last updated Jul 19, 2026
DRAG DROP
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You need to analyze video content to identify any mentions of specific company names.
Which three actions should you perform in sequence? To answer, move the appropriate action...
Author: Liam · Last updated Jul 19, 2026
You have a mobile app that manages printed forms.
You need the app to send images of the forms directly to Forms Recognizer to extract relevant information. For compliance reasons, the image files must not be stored i...
To send images of forms directly to the Forms Recognizer API while ensuring compliance (i.e., not storing images in the cloud), the best approach is to send the images in raw image binary format. Let’s break down the reasoning for each option:
Option A: Raw image binary
- Reasoning: The raw image binary format allows you to send the image directly in the request body, without the need for storing it in the cloud. This is especially important in your scenario, where images should not be stored in the cloud for compliance reasons.
- Key Factor: Sending raw image binary enables you to upload the images as they are, directly from the app, without needing intermediate cloud storage. This approach is both efficient and secure, maintaining compliance with the requirement to avoid cloud storage.
- Scenario: This is the ideal scenario for compliance and privacy, where the app handles and sends the raw binary data directly to the Forms Recognizer API for processing.
Option B: Form URL encoded
- Reasoning: The form URL encoded option is typically used for sending small data (like form submissions) via HTTP POST requests. It’s not suitable for sending large image files, as URL encoding adds overhead and may increase the size of the request, and it’s not the best option for sending raw binary data.
- Key Factor: URL encoding is more suitable for text-based data, not for binary files like images. Additionally, using URL encoding may expose data in a wa...
Author: Liam123 · Last updated Jul 19, 2026
You plan to build an app that will generate a list of tags for uploaded images. The app must meet the following requirements:
* Generate tags in a user's preferred language.
* Support English, French, and Spanish.
* Minimize development effort.
You ne...
To build an app that generates tags for uploaded images, taking into account the requirements to support multiple languages (English, French, and Spanish) and minimize development effort, the best Azure service to use is Computer Vision Image Analysis. Let's break down why this is the best option and explain why the others are less suitable.
Option A: Content Moderator Image Moderation
- Reasoning: Content Moderator is focused on identifying inappropriate content in images, such as adult content, violence, or racy material. It’s not designed to generate tags or analyze images for object recognition or general content categorization.
- Key Factor: It’s useful for moderation, not for generating descriptive tags based on image content.
- Scenario: Best for filtering out inappropriate content in images, but not for generating descriptive tags or performing object analysis.
Option B: Custom Vision Image Classification
- Reasoning: Custom Vision is a service that allows you to train a custom image classification model. While it can generate labels based on trained models for specific objects or concepts, it requires significant development and training time. You would need to label and train a model for each tag, which can be a time-consuming task.
- Key Factor: Custom Vision is a great option for specific image classification needs when you need to categorize images into predefined categories, but it requires custom training data and ongoing management of models. It also doesn't automatically handle multiple languages.
- Scenario: This option is useful for more specific, custom use cases, but it requires considerable effort to create custom models, and it does not inherently support multi-language tagging.
Option C: Computer Vision Image Analysis
- Reasoning: Com...
Author: Olivia · Last updated Jul 19, 2026
SNAPSHOT
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You develop a test method to verify the results retrieved from a call to the Computer Vision API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment.
For each of the fo...
Author: Ethan Smith · Last updated Jul 19, 2026
DRAG DROP
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You have a factory that produces cardboard packaging for food products. The factory has intermittent internet connectivity.
The packages are required to include four samples of each product.
You need to build a Custom Vision model that will identify defects in packaging and provide the location of the defects to an operator. The model must ensure that each package contains the four products.
Which project type and domain should you use? To answer, drag the appropriate opti...
Author: RadiantJaguar56 · Last updated Jul 19, 2026
SNAPSHOT
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You are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statem...
Author: Amira99 · Last updated Jul 19, 2026
You are building an app that will include one million scanned magazine articles. Each article will be stored as an image file.
You need to configure the app to extract text from the images. The s...
To configure the app to extract text from the images of one million scanned magazine articles with minimal development effort, the best solution is B) the Read API in Computer Vision. Let’s break down the reasoning for each option and why the selected one is ideal:
Option A: Computer Vision Image Analysis
- Reasoning: The Computer Vision Image Analysis service offers general image analysis, such as object detection and scene recognition. While it can extract some information from images, it’s not specifically optimized for extracting text from scanned documents or images.
- Key Factor: Although it has some text recognition capabilities (OCR), it doesn’t provide the targeted functionality and optimization that the Read API offers for extracting text from documents.
- Scenario: This option is useful for broad image analysis, but it's not as specialized for text extraction from scanned documents, making it less optimal for the task.
Option B: The Read API in Computer Vision
- Reasoning: The Read API in Computer Vision is specifically designed for extracting text from scanned documents and images. It supports Optical Character Recognition (OCR) and is highly optimized for extracting text from different types of images, including magazines and scanned articles. It also automatically detects the layout and structure of text in the images, which is critical when dealing with magazine articles.
- Key Factor: The Read API is the best fit for this scenario because it is optimized for text extraction, requires minimal configuration, and reduces development effort. It can handle large-scale image data efficiently, which is essential when working with a large number of images (one million articles).
- Scenario: Best used for extracting text from scanned documents and images with...
Author: Aarav · Last updated Jul 19, 2026
You have a 20-GB video file named File1.avi that is stored on a local drive.
You need to index File1.avi by using the Azu...
To index a 20-GB video file (File1.avi) using Azure Video Indexer, the first step should be to upload the video to the Azure Video Indexer website. Let’s break down why this is the best option and why the others are less suitable:
Option A: Upload File1.avi to an Azure Storage queue
- Reasoning: An Azure Storage queue is typically used for handling message-based processing, not for video uploads. While Azure Video Indexer can integrate with Azure Blob Storage (rather than a queue), a storage queue isn’t the correct location for uploading video files.
- Key Factor: You would need to upload the file to Azure Blob Storage, not a queue, for integration with Azure Video Indexer. This approach still requires an additional step of transferring the video from the queue to the service.
- Scenario: This option is not directly relevant to uploading videos for indexing with Azure Video Indexer.
Option B: Upload File1.avi to the Azure Video Indexer website
- Reasoning: The Azure Video Indexer website allows you to directly upload video files for analysis and indexing. Once uploaded, the service processes the video and provides insights such as speech-to-text, facial recognition, and sentiment analysis, among other things. This is the most straightforward approach to index a video file.
- Key Factor: Azure Video Indexer provides a simple interface for uploading and indexing videos directly, which is optimal for scenarios like yours where you have a large video file that needs to be indexed.
- Scenario: This is the ideal option because it directly supports video file uploads for indexing.
Opt...
Author: Olivia · Last updated Jul 19, 2026
SNAPSHOT
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You are building an app that will share user images.
You need to configure the app to meet the following requirements:
* Uploaded images must be scanned and any text must be extracted from the images.
* Extracted text must be analyzed for the presence of profane language.
* The solution must minimize development effort.
What...
Author: Ishaan · Last updated Jul 19, 2026
You are building an app that will share user images.
You need to configure the app to perform the following actions when a user uploads an image:
* Categorize the image as either a photograph or a drawing.
* Generate a caption for the image.
The solution must minimize development effort.
Which two services ...
To build an app that categorizes images as either a photograph or a drawing and generates a caption for the image, you would want to use services that can help with both image classification (e.g., distinguishing between photograph and drawing) and automatic image description (e.g., generating captions).
Let's analyze the options:
A) Object detection in Azure AI Computer Vision
- Explanation: Object detection is typically used to detect specific objects (e.g., people, animals, vehicles) in images. It does not perform categorization like distinguishing between a photograph and a drawing, nor does it generate captions for images.
- Why rejected: Object detection does not address both categorization (photograph vs. drawing) or caption generation directly.
B) Content tags in Azure AI Computer Vision
- Explanation: Content tags provide keywords related to the objects or themes present in an image. While this might provide some description, it doesn't categorize images as a photograph or a drawing, nor does it generate full captions.
- Why rejected: This option can help with descriptions, but it doesn't directly solve the categorization or caption generation requirement.
C) Image descriptions in Azure AI Computer Vision
- Explanation: Image descriptions are generated aut...
Author: David · Last updated Jul 19, 2026
You are building an app that will use the Azure AI Video Indexer service.
You plan to train a language model to recognize industry-specific terms.
You need to upload a file t...
When using the Azure AI Video Indexer service, particularly for training a language model to recognize industry-specific terms, you need to upload a file that contains those terms in a format that Azure can process efficiently.
Let's evaluate the file formats:
A) XML
- Explanation: XML is a structured markup language used to store data in a hierarchical format. While it's powerful for representing structured data, Azure AI Video Indexer does not typically expect XML files for language model training, as it is more commonly used for structured information like metadata or configurations.
- Why rejected: Not commonly used for uploading simple term lists. XML is typically more complex and might not be the easiest for the task of simply adding terms.
B) TXT
- Explanation: A plain text file is a simple and straightforward option for providing a list of terms. Azure AI Video Indexer can easily process a text file where each term is on a new line or separated by spaces or commas. It's efficient, lightweight, and supports the straightforward upload of terms that the language model can learn.
- Why selected: TXT files are ideal for uploading a list of terms because they ar...
Author: ThunderBear · Last updated Jul 19, 2026
DRAG DROP
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You have an app that uses Azure AI and a custom trained classifier to identify products in images.
You need to add new products to the classifier. The solution must meet the following requirements:
* Minimize how long it takes to add the products.
* Minimize development effort.
Which five actions should you...
Author: Olivia Johnson · Last updated Jul 19, 2026
SNAPSHOT
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You are developing an application that will use the Azure AI Vision client library. The application has the following code.
For each of the following statements, select Yes if the sta...
Author: Victoria · Last updated Jul 19, 2026
You are developing a method that uses the Azure AI Vision client library. The method will perform optical character recognition (OCR) in images. The method has the following code.
During testing, you discover that the call to the get_read_result method occurs before the read operation is complete.
You need to prevent the get_read_result method from proceeding until the read ...
Author: IceDragon2023 · Last updated Jul 19, 2026
SNAPSHOT
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You are developing an app that will use the Azure AI Vision API to analyze an image.
You need configure the request that will be used by the app to identify whether an image is clipart or a line drawing.
How should you complete the request? ...
Author: Lina Zhang · Last updated Jul 19, 2026
SNAPSHOT
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You have an Azure subscription that contains an Azure AI Video Indexer account.
You need to add a custom brand and logo to the indexer and configure an exclusion for the custom brand.
How should you complete the REST API call? To a...
Author: Ava · Last updated Jul 19, 2026
You have a local folder that contains the files shown in the following table.
You need to analyze the files by using Azure AI Video Index...
Author: Max · Last updated Jul 19, 2026
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You build a language model by using a Language Understanding service. The language model is used to search for information on a contact list by using an intent named FindContact.
A conversational expert provides you with the following ...
In this scenario, you are using Language Understanding (LUIS) to create an intent called FindContact and you want to use a list of phrases to train the model to identify specific search requests for contacts based on location (e.g., "London," "Seattle," "Ukraine").
The key here is to determine if creating a new pattern for the FindContact intent will meet the goal.
Let's break it down:
Explanation of the solution:
A) Yes
- Reasoning: When you're training a language model using Language Understanding (LUIS), you define intents that capture user goals and provide examples (or patterns) of how users might express those goals. A pattern is a specific form of training example that allows LUIS to recognize how certain words or phrases match the intent.
- FindContact intent: If you want to recognize queries like "Find contacts in London," "Who do I know in Seattle," and "Search for contacts in Ukraine," a pattern could be used to capture those types of phrases. A ...
Author: VioletCheetah55 · Last updated Jul 19, 2026
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You develop an application to identify species of flowers by traini...
In this scenario, you are developing an application to identify species of flowers by training a Custom Vision model, and you want to add new images of flowers to the classifier.
Evaluation of the solution:
Smart Labeler Tool in Custom Vision:
- The Smart Labeler tool is designed to assist in labeling new images when training a Custom Vision model. It helps by suggesting labels for the new images based on the existing labeled data in the model.
- It can automatically predict what label (e.g., flower species) should be applied to the new images based on the model's prior training.
- After adding the new images, using the Smart Labeler tool helps in labeling the images correctly, thus making the process of adding new data more efficient.
Explanation of the solution:
A) Yes
- Reasoning: The solution meets the goal because after adding the new images to the training set, the Smart Labeler tool will automatically suggest the correct labels...
Author: Ahmed · Last updated Jul 19, 2026
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You develop an application to identify species of flowers by training a Custom Vision model.
You re...
In this scenario, the goal is to add new images and labels to an existing flower species classifier and retrain the model to identify new species.
Let's evaluate the solution:
1. Adding new images and labels: This is the correct step for introducing new data to the model. New images and their associated labels need to be added to the training data so that the model can learn to identify the new species.
2. Retraining the model: After adding the new images and labels, retraining the model is necessary to ensure that the model is updated with the new data. This ensures the model can classify both old and new species accurately.
3. Publishing the model: Once the retraining process is complet...
Author: Mia · Last updated Jul 19, 2026
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You develop an application to identify species of flowers by training a ...
The solution described in the scenario involves creating a new model and then uploading the new images and labels. Let’s break down the reasoning:
Analysis of the Solution:
- Creating a new model: If you create a new model, this means you are starting fresh and training the model from scratch with the new images and labels, rather than updating the existing model.
- Uploading new images and labels: This step is essential in adding the new data, but if you create a new model, you're not leveraging the existing model that already contains knowledge of the previous flower species.
Does this meet the goal?
The goal is to add new images to a...
Author: Ethan · Last updated Jul 19, 2026
SNAPSHOT -
You are developing a service that records lectures given in English (United Kingdom).
You have a method named AppendToTranscriptFile that takes translated text and a language identifier.
You need to develop code that will provide transcripts of the lectures to attendees in their respective language. The supported languages are English, French,
Spanish, and Ger...
Author: Krishna · Last updated Jul 19, 2026
DRAG DROP -
You train a Custom Vision model used in a mobile app.
You receive 1,000 new images that do not have any associated data.
You need to use the images to retrain the model. The solution must minimize how long it takes to retrain the model.
Which three actions should you perform in the Custom Vision portal? To...
Author: Ryan · Last updated Jul 19, 2026
You are building a Conversational Language Understanding model for an e-commerce chatbot. Users can speak or type their billing address when prompted by the chatbot.
You need to const...
To capture billing addresses in a Conversational Language Understanding (CLU) model for an e-commerce chatbot, the entity needs to identify and extract structured information like street address, city, state, and zip code from user inputs, whether they are spoken or typed.
Let's evaluate each entity type:
1. A) Machine learned:
- Machine learned entities are typically used when the entity needs to recognize complex, varied, and unstructured data, such as dates, locations, or other highly variable inputs. In the case of a billing address, which is relatively structured but can have variations in phrasing and formatting, a machine learning-based approach could be useful, but it would require more training data and fine-tuning. This is a good option for extracting structured information like addresses, especially if the addresses are expected to vary in format.
2. B) Regex:
- Regex (regular expressions) are very useful for extracting structured patterns (like phone numbers, dates, or specific formats). However, addresses can have varied formats (e.g., different ways of writing the street, city, or state), so using Regex would require very complex and potentially error-prone patterns. It's not the best choice for billing addresses, as the format can differ from user to user and may not be fully captured by a single regex pattern.
3. C) List:
- A List entity type is used when there is a ...
Author: Daniel · Last updated Jul 19, 2026
You are building an Azure WebJob that will create knowledge bases from an array of URLs.
You instantiate a QnAMakerClient object that has the relevant API keys and assign the object to a variable named client.
You need to develop a method to create the knowledge bases.
Which two actions should...
In the context of developing an Azure WebJob that creates knowledge bases from an array of URLs using the QnAMakerClient, the solution requires understanding the process of creating a knowledge base (KB) in QnA Maker.
Option A: Create a list of FileDTO objects that represents data from the WebJob.
- Reasoning: This option is incorrect because FileDTO is not the standard object needed to create a knowledge base in QnA Maker. To create a knowledge base, you do not need to first create a list of FileDTO objects; instead, you will need to prepare data in a specific format (such as QnADTO or CreateKbDTO) which will be used in the process.
Option B: Call the client.Knowledgebase.CreateAsync method.
- Reasoning: This option is correct because the method `CreateAsync` is responsible for creating a knowledge base in QnA Maker. This method requires data in the form of `CreateKbDTO` or QnADTO objects to create the knowledge base asynchronously. Without calling `CreateAsync`, the knowledge base won't be created. This is the critical step in the process.
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