Learn about the key steps in the data ingestion and preparation stage of an Azure Machine Learning workflow, including cleaning data and merging datasets. Prepare your data properly for successful model training.
Table of Contents
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
In an Azure Machine Learning process, the data ingestion and data preparation phase involves importing your raw data and getting it ready for training machine learning models. The two key actions performed at this stage are:
1. Remove records that have missing values (Option C)
When you ingest data, it often contains missing, incomplete or invalid values. Data cleaning is a crucial step where you identify and remove or impute records with missing data to ensure your dataset is high-quality before training.
2. Combine multiple datasets (Option E)
Data for your ML project frequently comes from multiple sources. During data preparation, you join and merge relevant datasets together to create the final training data. This could involve concatenating tables, looking up related records, aggregating data, etc.
Other steps like calculating model accuracy, generating predictions, and scoring test data happen later in the machine learning lifecycle during model training and evaluation. So options A, B and D do not apply to the data preparation phase.
In summary, the data ingestion and preparation stage is all about obtaining your data, cleaning it, and combining it into the final form needed to train your machine learning models in Azure ML. Proper data preparation is essential for successful model development and high prediction accuracy.
These actions are essential for preparing the data before training a machine learning model.
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