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| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe machine learning pipelines |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Identify types of NLP solutions |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
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NEW QUESTION # 224
Match the computer vision service to the appropriate Al workload.
To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
This question evaluates understanding of the different Azure AI Computer Vision services and their distinct functionalities, as covered in the Microsoft AI-900 study guide and Microsoft Learn modules under "Describe features of common AI workloads" and "Identify Azure services for computer vision."
* Azure AI Document Intelligence (formerly known as Form Recognizer):This service is designed to extract structured information from documents, such as forms, receipts, and invoices. It uses optical character recognition (OCR) combined with AI models to detect key-value pairs, tables, and handwritten text. This makes it ideal for automating data entry and digitizing scanned documents.
Hence, it matches "Extract information from scanned forms and invoices."
* Azure AI Vision (formerly Computer Vision):This service provides image and video analysis capabilities. It can detect objects, people, text, and scenes; generate image captions; and extract descriptive tags. It also supports OCR for printed and handwritten text within images. Therefore, it matches "Analyze images and video, and extract descriptions, tags, objects, and text."
* Azure AI Custom Vision:Custom Vision allows you to train your own image classification and object detection models using your own labeled images. Unlike the general Vision service, Custom Vision lets you build domain-specific models-for example, detecting your company's products or identifying manufacturing defects. Hence, it matches "Train custom image classification and object detection models by using your own images." These three services complement each other within Azure's computer vision ecosystem, collectively supporting both general-purpose and specialized AI solutions for visual data analysis.
NEW QUESTION # 225
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the standard workflow for creating and deploying a machine learning model - especially within Azure Machine Learning Designer - follows a structured sequence of steps to ensure that the model is trained effectively and evaluated correctly.
Here's the detailed breakdown of the correct order:
* Import and prepare a dataset:This is always the first step in the machine learning lifecycle. The dataset is imported into Azure Machine Learning and cleaned or preprocessed. Preparation might include handling missing values, normalizing data, removing outliers, and encoding categorical variables. This ensures the dataset is ready for modeling.
* Split the data randomly into training data and validation data:The dataset is then divided into two parts
- the training set and the validation (or testing) set. Typically, around 70-80% of the data is used for training and 20-30% for validation. This step ensures that the model can be evaluated on unseen data later, preventing overfitting.
* Train the model:During this stage, the machine learning algorithm learns patterns from the training data. Azure Machine Learning Designer provides multiple algorithms (classification, regression, clustering, etc.) that can be applied using "Train Model" components.
* Evaluate the model against the validation dataset:Finally, the trained model's performance is tested using the validation dataset. Evaluation metrics such as accuracy, precision, recall, or RMSE (depending on the model type) are calculated to assess how well the model generalizes to new data.
The incorrect option - "Evaluate the model against the original dataset" - is not used in proper ML workflows, because evaluating on the same data used for training would give misleadingly high accuracy due to overfitting.
NEW QUESTION # 226
You have an app that identifies the coordinates of a product in an image of a supermarket shelf.
Which service does the app use?
Answer: B
NEW QUESTION # 227
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.
Which type of computer vision was used?
Answer: A
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 228
Your company manufactures widgets.
You have 1.000 digital photos of the widgets.
You need to identify the location of the widgets within the photos.
What should you use?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," object detection is a computer vision technique used to locate and identify objects within an image. It not only determines what objects are present but also where they appear in the image by returning bounding box coordinates around each detected item.
In this scenario, the goal is to identify the location of widgets within digital photos. This requires both recognition (knowing that the object is a widget) and localization (determining its position). The Custom Vision service in Azure allows you to train a model specifically for your own images, making it ideal for recognizing company-specific products such as widgets. By selecting the Object Detection domain in Custom Vision, you can label regions of interest in your training images. The model then learns to detect and locate those objects in new photos.
Let's examine the other options:
* A. Computer Vision Spatial Analysis: Used for people tracking, movement detection, and occupancy analytics in video streams - not for locating products in still images.
* C. Custom Vision classification: This model categorizes an image as a whole (e.g., "contains a widget" or "does not contain a widget") but does not locate objects within the image.
* D. Computer Vision Image Analysis: Provides general image tagging, description, and OCR capabilities but does not pinpoint object locations.
NEW QUESTION # 229
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