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| Section | Weight | Objectives |
|---|---|---|
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities - Describe core machine learning concepts |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
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NEW QUESTION # 314
You have an Azure subscription that uses Azure OpenAI.
You need to create an original image of a rural scene to use on a website.
What should you do?
Answer: B
NEW QUESTION # 315
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Explanation
A: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets.
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
D: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION # 316
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis
NEW QUESTION # 317
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:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 318
You have a large dataset that contains motor vehicle sales data.
You need to train an automated machine learning (automated ML) model to predict vehicle sale values based on the type of vehicle.
Which task should you select? To answer, select the appropriate task in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning documentation, regression is the appropriate machine learning task when the goal is to predict continuous numeric values- such as prices, sales amounts, or other measurable quantities.
In this scenario, the dataset contains motor vehicle sales data, and the objective is to predict vehicle sale values. A vehicle's sale value (price) is a continuous numeric variable, meaning it can take on a wide range of possible numbers (for example, $15,000, $28,500, $42,300). Regression models are designed to analyze relationships between input features (like make, model, mileage, age, fuel type) and a continuous output variable (price).
Automated Machine Learning (AutoML) in Azure simplifies this process by automatically testing multiple regression algorithms (e.g., Linear Regression, Random Forest, Gradient Boosted Trees) and hyperparameters to find the best-performing model.
Let's evaluate why the other options are incorrect:
* Classification - Used for predicting discrete categories (e.g., car type: sedan, SUV, or truck). Sale value prediction is not categorical.
* Time series forecasting - Used when predicting future values based on time-dependent data (e.g., sales over months or years). The question focuses on predicting price based on vehicle characteristics, not over time.
* Natural Language Processing (NLP) - Deals with text-based data, not numeric vehicle data.
* Computer Vision - Applies to image-based tasks (e.g., detecting car types from photos).
Therefore, per Microsoft Learn's Automated ML task selection guidance, when predicting a numeric output like vehicle sale value, the correct machine learning task type is Regression.
# Final answer: Regression
NEW QUESTION # 319
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