AI-900: What Actions Are Performed in Data Ingestion & Preparation in Azure ML?

Learn about the crucial data ingestion and preparation steps in Azure Machine Learning, including removing missing values and combining datasets for optimal model performance.

Question

Which actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Select two correct options.

A. Calculate the accuracy of the model.
B. Use the model for real-time predictions.
C. Remove records that have missing values.
D. Score test data by using the model.
E. Combine multiple datasets.

Answer

C. Remove records that have missing values.
E. Combine multiple datasets.

Explanation

Data ingestion and data preparation are critical stages in the Azure Machine Learning process that ensure the quality and compatibility of the data used to train machine learning models. The two key actions performed during this stage are:

1. Remove records that have missing values (Option C): Incomplete or missing data can negatively impact the performance of machine learning models. During data preparation, records with missing values are identified and either removed or imputed with estimated values to maintain data integrity.
2. Combine multiple datasets (Option E): Often, the data required to train a model may come from various sources. In the data ingestion and preparation stage, these disparate datasets are combined and integrated to create a comprehensive and unified dataset that can be used for training the machine learning model.

Options A, B, and D are not part of the data ingestion and preparation stage. Calculating model accuracy (A) and scoring test data (D) are performed during the model evaluation stage, while using the model for real-time predictions (B) occurs during the deployment and inference stage.

By focusing on removing missing values and combining datasets during data ingestion and preparation, Azure Machine Learning ensures that the data used to train models is of high quality and well-suited for the task at hand, ultimately leading to better model performance and more accurate predictions.

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