Discover the key AutoML tasks supported in Microsoft Azure AI-900 certification, including Natural Language Processing and Computer Vision. Learn how these capabilities enhance AI projects and boost your career prospects.
Table of Contents
Question
What tasks are supported by AutoML? (Choose two.)
A. Natural Language Processing (NLP)
B. Clustering
C. Computer vision
D. Generative modeling
E. Deep learning with unlabeled data
Answer
A. Natural Language Processing (NLP)
C. Computer vision
Explanation
The two tasks supported by AutoML are:
- Computer vision: AutoML can automate the process of building models for tasks such as image classification (e.g., recognizing objects in images) and object detection (e.g., locating objects in images). It offers pre-built features for specific scenarios and can handle various image-related tasks.
- Natural Language Processing (NLP): AutoML supports building NLP models for tasks such as text classification (e.g., sentiment analysis) and named entity recognition (e.g., identifying names and locations in text). It leverages pre-trained language models and customizable feature engineering, allowing you to analyze and extract meaning from textual data.
Clustering belongs to the unsupervised learning category, where data lacks labeled outputs. AutoML currently focuses on supervised learning scenarios, where data points have predefined labels for predicting specific outcomes.
Deep learning with unlabeled data is not supported by AutoML. While deep learning can be used in AutoML pipelines, the crucial element is labeled data for training supervised models. AutoML does not handle tasks requiring unlabeled data analysis.
Generative modeling is not supported by AutoML. This involves creating new data based on existing data patterns using unsupervised learning techniques. As with unlabeled data, it falls outside the scope of AutoML’s supervised learning focus.
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