You have an Azure OpenAI model named AI1. You are building a web app named App1 by using the Azure OpenAI SDK. You need to configure A...

To configure App1 to connect to AI1 (Azure OpenAI model), you need to provide specific information related to the deployment and connection details of your Azure OpenAI instance. Here's an analysis of each option: A) the endpoint, key, and model name This option suggests that you need to provide the endpoint, API key, and model name. - Reasoning: While the endpoint and key are necessary for authentication and connecting to the Azure service, model name alone is insufficient for connecting to a specific deployment. You need to specify the deployment that hosts the model. Model name refers to the type or version of the model (e.g., GPT-4), but deployment name is a key element to pinpoint which instance of the model you are interacting with in Azure. B) the deployment name, key, and model name This option includes the deployment name, API key, and model name. - Reasoning: While this is closer to the correct configuration, it is missing the endpoint. The endpoint is essential to connect to the Azure OpenAI service, as it tells the app where the model is hosted. Without the endpoint, the app won't know where to send requests. C) t...

Author: Noah · Last updated May 3, 2026

You are building a solution in Azure that will use Azure Cognitive Service for Language to process sensitive customer data. You need to ensure that only specific Azure processes can access the Language service. ...

To ensure that only specific Azure processes can access the Azure Cognitive Service for Language, you need to limit access to the service based on network conditions, specifically ensuring that access is restricted to a designated virtual network. Let's analyze each option: A) IPsec rules IPsec (Internet Protocol Security) is used to secure network traffic by encrypting it and providing authentication at the IP layer. While it can be used to secure communication between different endpoints, it doesn't directly control access to a specific Azure service like Cognitive Services. - Reasoning: IPsec would secure the communication but does not provide a mechanism to control access based on network identity or restrict access to Azure Cognitive Services. B) Azure Application Gateway The Azure Application Gateway is a web traffic load balancer that can manage web traffic and secure applications. While it’s useful for routing and security at the application layer (Layer 7), it is not specifically designed for controlling access to Azure Cognitive Services. It can be used for managing web traffic but doesn't directly manage access to Azure Cognitive Services. - Reasoning: While Application Gateway is helpful in load balancing and securing traffic, it is not the correct choice for controlling access to Azure Cognitive Services based on specific network rules. C) A virtual network gateway A virtual network gateway is used for connecting on-premises networks to Azure through a VPN or ExpressRoute. While it can...

Author: Amira99 · Last updated May 3, 2026

You plan to perform predictive maintenance. You collect IoT sensor data from 100 industrial machines for a year. Each machine has 50 different sensors that generate data at one-minute intervals. In total, you have 5,000 time series datasets. You need to iden...

To identify unusual values in time series data for predictive maintenance, the most suitable Azure service would be Azure AI Anomaly Detector. Here’s an analysis of each option: A) Azure AI Computer Vision Azure AI Computer Vision is a service designed for analyzing images and video, detecting objects, reading text, and extracting visual data from pictures. It is specifically tailored for tasks related to visual content, such as image classification, object detection, and optical character recognition (OCR). - Reasoning: This service is not suited for time series data, such as sensor readings or numerical data over time. It is meant for visual data processing and does not have the capabilities to handle or analyze the type of data you have (sensor readings for predictive maintenance). B) Cognitive Search Azure Cognitive Search is a fully-managed search-as-a-service that allows you to create search indexes for unstructured content, such as documents, web pages, and other text-based data. It is used to enable search and retrieval of data based on user queries. - Reasoning: While Cognitive Search excels at searching large datasets of text or documents, it is not designed for anomaly detection in time series or sensor data. Anomaly detection requires specialized models for numerical and sequential data, not just search capabilities. C) Azure AI Document Intelli...

Author: Maya · Last updated May 3, 2026

HOTSPOT - You plan to deploy a containerized version of an Azure Cognitive Services service that will be used for sentiment analysis. You configure https://contoso.cognitiveservices.azure.com as the endpoint URI for the service. You need to run the container on an Azure virtual machine by using Docker. How shoul...

Author: Ethan · Last updated May 3, 2026

You are developing a system that will monitor temperature data from a data stream. The system must generate an alert in response to atypical values. The solution must ...

Evaluation of Options: A) Multivariate Anomaly Detection: - Explanation: Multivariate anomaly detection analyzes multiple variables at once to detect anomalies based on relationships between them. If the temperature data is collected with other metrics (e.g., humidity, pressure), multivariate anomaly detection would consider the correlation between these variables to identify atypical patterns. - When to use: This would be ideal if you are dealing with data streams that involve multiple variables, and the anomaly detection needs to consider how they interact. - Why it might not be the best option: For a simple temperature data stream with only a single variable (temperature), multivariate anomaly detection adds unnecessary complexity, and the additional effort to configure and use might outweigh the benefits. B) Azure Stream Analytics: - Explanation: Azure Stream Analytics can process real-time data streams and apply conditions like anomaly detection. It allows you to filter, aggregate, and analyze data in real time. - When to use: This is an excellent choice if you need to monitor real-time data streams and apply complex transformation or anomaly detection. It can handle multiple data streams and allow complex SQL-like queries to create alerts based on specific conditions. - Why it might not be the best option: If the goal is simply to identify atypical temperature data and the system doesn't need complex transformations or multi-source integration, Stream Analytics can introduce more complexity than necessary. It requires more configuration and monitoring, adding to development effort. C) Metric Alerts in Azure Monitor: - Explanation: Azure Monitor provides metric alerts that allow you to define thresholds for specific metrics and generate alerts when those thresholds are breached. This is a simple way to monitor a single variable, such as temperature, for specific thresholds. - When to use:...

Author: Maya2022 · Last updated May 3, 2026

You have a Microsoft OneDrive folder that contains a 20-GB video file named File1.avi. You need to index File1.avi by using ...

Evaluation of Options: A) Upload File1.avi to the www.youtube.com webpage, and then copy the URL of the video to the Azure AI Video Indexer website: - Explanation: You can upload the video to YouTube and then use the URL in the Azure AI Video Indexer. Azure Video Indexer can retrieve videos from YouTube and index them automatically. - When to use: This option would work if you are fine with uploading the video to YouTube and have no restrictions on privacy or content, but it is not the most direct way to handle your own private file. - Why it might not be the best option: Since the video is stored in OneDrive and does not need to be uploaded to YouTube, this method introduces an unnecessary third-party platform and could potentially compromise privacy or require additional configuration steps. Moreover, it involves extra steps when the video can be directly uploaded to the Azure Video Indexer. B) Download File1.avi to a local computer, and then upload the file to the Azure AI Video Indexer website: - Explanation: You can download the video file from OneDrive to a local machine and then upload it directly to Azure AI Video Indexer. - When to use: This option works for directly uploading the file if you prefer or need to work with a local copy of the video before indexing. - Why it might not be the best option: Downloading the file and re-uploading it requires additional steps, consumes local bandwidth, and potentially wastes time when the file is already stored in OneDrive. The process is not as efficient as other direct cloud-based options. C) From OneDrive, create a download link, and then copy the link to the Azure AI Video Indexer website: - Explanation: This option involves creating a download link for the video stored on OneDrive and copying that link into the Azure AI Video Indexer for indexing. - When to use: This could be a potential solution if A...

Author: Benjamin · Last updated May 3, 2026

You have an Azure subscription that contains an Azure AI Service resource named CSAccount1 and a virtual network named VNet1. CSAaccount1 is connected to VNet1. You need to ensure that only specific resources can access CSAccount1. The solution must meet the following requirements: * Prevent external access to CSAccount1. * Minimize administrat...

Evaluation of Options: A) In VNet1, enable a service endpoint for CSAccount1: - Explanation: Enabling a service endpoint in the virtual network allows you to restrict the access to CSAccount1 from only specific resources within the virtual network (VNet1). The service endpoint effectively limits access to CSAccount1 to only those resources that are connected to VNet1, preventing external access. - When to use: This option is directly relevant to the scenario where you need to prevent external access to CSAccount1 while ensuring access from specific internal resources. - Why it's selected: This is a good solution because enabling service endpoints is a simple and effective way to secure Azure services like CSAccount1 within the virtual network, minimizing administrative effort by not requiring complex access control configurations. B) In CSAccount1, configure the Access control (IAM) settings: - Explanation: IAM (Identity and Access Management) settings are used to control who has access to the resource, and what level of permissions they have. While IAM can help with granular access control, it doesn't directly address the requirement to prevent external access from outside VNet1. - When to use: IAM is useful for controlling user-level or service principal-level access to resources, but it doesn't directly prevent external access or ensure that only resources within the virtual network can access CSAccount1. - Why it's rejected: IAM doesn't address network-level security or prevent access from outside the network, which is a critical requirement for this scenario. C) In VNet1, modify the virtual network settings: - Explanation: Modifying virtual network settings could involve configurations related to subnetting, IP address ranges, or network security. While this could help control how traffic flows within the VNet, it doesn't directly enforce the restriction that only specific resources can access CSAccount1. - When to use: This could be useful for general network management but doesn't provide the specific solution for preventing external access to CSAccount1. - Why it's rejected: Modifying virtual network settings alone doesn't provide a clear, efficient method for restricting access to ...

Author: Rahul · Last updated May 3, 2026

You are building an internet-based training solution. The solution requires that a user's camera and microphone remain enabled. You need to monitor a video stream of the user and detect when the user asks an instructor a qu...

Evaluation of Options: A) Speech-to-text in the Azure AI Speech service: - Explanation: Azure's Speech-to-Text service is designed to transcribe spoken words into text in real time. By leveraging this service, you could analyze the video stream's audio to detect when the user asks a question, based on specific speech patterns or keywords. - When to use: This is ideal for the scenario, as you need to monitor the audio (microphone) and detect specific verbal interactions, such as questions from the user. The service would convert speech into text, which could then be analyzed for keywords (e.g., "what," "how," "can you explain," etc.). - Why it's selected: It directly addresses the requirement to monitor when a user asks a question, using speech recognition to transcribe the audio and detect key question-related phrases, minimizing development effort while meeting the solution's requirements. B) Language detection in Azure AI Language Service: - Explanation: The Azure AI Language Service can detect the language of text but is not designed to handle speech or real-time audio. This would be more relevant if you had already transcribed the speech into text and wanted to analyze the language or intent afterward. - When to use: This could be used if you needed to analyze the text of questions for specific language-related features (like detecting which language a question is in), but it doesn't directly address the core requirement to detect when a user asks a question. - Why it's rejected: Language detection doesn’t help with the real-time task of detecting verbal questions from the video stream. It’s not designed for speech recognition or real-time question detection. C) The Face service in Azure AI Vision: - Explanation: The Face service can detect faces in images or video streams and recognize facial features, expressions, and identities. However, it does not analyze aud...

Author: VioletCheetah55 · Last updated May 3, 2026

You have an Azure DevOps pipeline named Pipeline1 that is used to deploy an app. Pipeline1 includes a step that will create an Azure AI services account. You need to add a step to Pipeline1 that will identify the created Azure AI services account. The ...

Evaluation of Options: A) az resource link: - Explanation: The `az resource link` command is used for managing resource links, which are references to other resources within an Azure resource group. This command is not directly related to querying or retrieving information about an Azure AI services account. - When to use: This would be useful in scenarios where you need to link one resource to another (for example, linking a storage account to a virtual machine), but it is not suitable for identifying or retrieving details about a specific Azure AI services account. - Why it's rejected: The `az resource link` command does not help identify or show details about an Azure AI services account. B) az cognitiveservices account network-rule: - Explanation: The `az cognitiveservices account network-rule` command is used to manage network rules for an Azure Cognitive Services account, such as restricting access to specific IP addresses or virtual networks. While this is useful for configuring network access, it doesn’t provide information on identifying or viewing details of an AI services account. - When to use: This would be useful if you need to configure network restrictions or access rules for the AI services account, but it doesn't directly help identify or display account details. - Why it's rejected: This command is more focused on network security and doesn’t help in identifying the AI services account itself. C) az cognitiveservices account show: - Explanation: The `az cognitiveservices account show` command retrieves detailed information about an Azure Cognitive Services account, including its properties, status, and configuration. This is the most relevant command for identifying a specific Azure AI services account by providing detailed account info...

Author: Max · Last updated May 3, 2026

HOTSPOT - You have 1,000 scanned images of hand-written survey responses. The surveys do NOT have a consistent layout. You have an Azure subscription that contains an Azure AI Document Intelligence resource named AIdoc1. You open Document Intelligence Studio and create a new project. You need to extract data from the survey responses. The solution must minimize development effort. To where shoul...

Author: Carlos Garcia · Last updated May 3, 2026

HOTSPOT - 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: Ryan · Last updated May 3, 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: Chloe · Last updated May 3, 2026

HOTSPOT - 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: Kai · Last updated May 3, 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: Emma · Last updated May 3, 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: Ethan Smith · Last updated May 3, 2026

HOTSPOT - 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: MoonlitPantherX · Last updated May 3, 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: Oliver · Last updated May 3, 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: Amelia · Last updated May 3, 2026

HOTSPOT - 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 p...

Author: Ella · Last updated May 3, 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: Sophia · Last updated May 3, 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: Henry · Last updated May 3, 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: CrimsonViperX · Last updated May 3, 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: SilverBear · Last updated May 3, 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: FlamePhoenix2025 · Last updated May 3, 2026

HOTSPOT - 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: IceDragon2023 · Last updated May 3, 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: Abigail · Last updated May 3, 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: William · Last updated May 3, 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: Lucas · Last updated May 3, 2026

HOTSPOT - 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: Victoria · Last updated May 3, 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: Zara1234 · Last updated May 3, 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: Aarav2020 · Last updated May 3, 2026

HOTSPOT - 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 t...

Author: Ethan · Last updated May 3, 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: Ella · Last updated May 3, 2026

HOTSPOT - 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 in...

Author: Ishaan · Last updated May 3, 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: Vivaan · Last updated May 3, 2026

DRAG DROP - 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: Amelia · Last updated May 3, 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: Zara · Last updated May 3, 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: Deepak · Last updated May 3, 2026

HOTSPOT - 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: Aditya · Last updated May 3, 2026

DRAG DROP - 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: Olivia · Last updated May 3, 2026

HOTSPOT - 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 stateme...

Author: Ahmed97 · Last updated May 3, 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: Sofia2021 · Last updated May 3, 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: MysticJaguar44 · Last updated May 3, 2026

HOTSPOT - 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: Aarav2020 · Last updated May 3, 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: Emily · Last updated May 3, 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: Zara1234 · Last updated May 3, 2026

DRAG DROP - 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: Max · Last updated May 3, 2026

HOTSPOT - 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 stat...

Author: Stella · Last updated May 3, 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: Carlos Garcia · Last updated May 3, 2026

HOTSPOT - 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: Charlotte · Last updated May 3, 2026