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| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Identify Azure AI services for NLP - Describe Azure capabilities for NLP |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
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NEW QUESTION # 279
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
Answer: A
Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION # 280
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
This question tests understanding of AI workload types, a fundamental topic in the Microsoft Azure AI Fundamentals (AI-900) curriculum. Each workload type-Computer Vision, Natural Language Processing, Machine Learning (Regression), and Anomaly Detection-serves a specific function within the AI landscape, as explained in Microsoft Learn's module "Describe features of common AI workloads."
* Computer Vision enables computers to "see" and interpret visual information such as images or videos.
Identifying handwritten letters requires analyzing image patterns, shapes, and strokes, which is a classic image recognition task. Azure's Computer Vision API and Custom Vision services are specifically designed for such tasks.
* Natural Language Processing (NLP) involves interpreting human language, both written and spoken.
Determining the sentiment of a social media post (positive, negative, or neutral) is a typical text analytics use case within NLP, often implemented using Azure's Text Analytics for Sentiment Analysis.
* Anomaly Detection focuses on identifying data points that deviate from normal patterns. Detecting fraudulent credit card payments requires finding transactions that are unusual compared to historical spending behavior. Azure's Anomaly Detector API applies machine learning to identify such irregularities.
* Machine Learning (Regression) is used for predicting continuous numerical outcomes based on historical data. Estimating next month's toy sales is a regression problem-an example of supervised learning where the model predicts future sales values from past sales data.
Thus, based on Microsoft's official AI-900 learning objectives, the correct mapping of workloads to scenarios is:
* Computer Vision # Identify handwritten letters
* NLP # Predict sentiment
* Anomaly Detection # Fraud detection
* Machine Learning (Regression) # Predict toy sales
NEW QUESTION # 281
You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds. This is an example of which type of computer vision?
Answer: C
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied.
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.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer- overview
NEW QUESTION # 282
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
Answer: B
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
NEW QUESTION # 283
For each of the following statements, select Yes if the statement is True. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 284
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