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AI-900T00: Introduction to AI in Azure Quiz Questions and Answers

Answer :
  • By providing pre-built models for sentiment analysis and language detection

Explanation :

Microsoft Azure's Language Service enhances text analysis by offering pre-built models for common tasks such as sentiment analysis and language detection, which helps businesses quickly gain insights from text data.
Answer :
  • Custom Vision Service

Explanation :

Custom Vision Service is tailored for creating, deploying, and refining image classification models that are well-suited for specific tasks, such as identifying products in real-time inventory management settings.
Answer :
  • Azure Cognitive Services - Text Analytics

Explanation :

Azure Cognitive Services - Text Analytics is specifically designed for analyzing text data, extracting key phrases, entities, and analyzing sentiment, making it ideal for processing customer interaction logs.
Answer :
  • By offering pre-built APIs and models

Explanation :

Azure Cognitive Services provide pre-built APIs and models, enabling developers to leverage AI capabilities such as vision, speech, and language understanding without needing deep machine learning expertise.
Answer :
  • Mean squared error

Explanation :

Mean squared error (MSE) is used in linear regression to measure how well the model predicts the target variable, computed as the average of squared differences between predicted and actual values.
Answer :
  • Object Detection identifies objects and their positions, while Image Classification labels the entire image.

Explanation :

Object Detection focuses on locating objects in an image and determining their positions, whereas Image Classification assigns a label to the entire image without pinpointing object positions.
Answer :
  • When multiple entities in the text share similar names

Explanation :

Entity linking ensures that occurrences of similar or identical names within texts correctly refer to distinct real-world entities, thereby enhancing the reliability of extracted information.
Answer :
  • It enhances model accuracy and reliability

Explanation :

Data preprocessing transforms raw data into a clean dataset, ensuring that machine learning models are built on accurate, relevant, and consistent data, leading to improved performance.
Answer :
  • It increases the size of the dataset and introduces variability.

Explanation :

Data augmentation artificially increases the size of the training dataset by applying transformations, adding variability and improving the models ability to generalize to new data.
Answer :
  • It improves structured data retrieval from unstructured text.

Explanation :

Named Entity Recognition helps automatically identify and categorize key entities in text, making it easier to convert unstructured text into structured information for retrieval.