MLA-C01 Question 81
Select 3You are tasked with building a machine learning model using customer transactional data stored in an Amazon S3 bucket. During the data preparation phase, you discover that the dataset has missing values, duplicate records, and inconsistent formats for the 'date' column. Which of the following steps should you take to ensure data integrity and prepare the data for modeling?
- A
Remove duplicate records to avoid skewed model training.
- B
Fill missing values using appropriate imputation techniques, such as mean or median, based on the data type and distribution.
- C
Ignore the 'date' column entirely, as correcting the format is not necessary for modeling.
- D
Standardize the 'date' column to a consistent format to ensure correct feature processing.
- E
Directly pass the raw dataset to the model and rely on the model to handle data inconsistencies.
Show answer and explanation
Correct answers: A, B, D
Explanation
Ensuring data integrity involves handling issues like duplicates, missing values, and inconsistent formats. Removing duplicate records prevents bias, while imputing missing values ensures completeness. Standardizing the 'date' column ensures that it can be properly processed as a feature. Ignoring key data or relying on the model to handle inconsistencies is not a best practice, as these steps should be handled during the data preparation phase to build a robust and reliable model.
- A. Correct.
Removing duplicate records is a crucial step in ensuring data integrity, as duplicate entries can lead to biased or skewed model training.
- B. Correct.
Filling missing values is essential for handling incomplete data, as many machine learning algorithms cannot handle missing values directly. The choice of imputation technique should depend on the data type and distribution.
- C. Incorrect.
Ignoring the 'date' column would result in the loss of potentially important features. Data consistency is vital for accurate feature processing.
- D. Correct.
Standardizing the 'date' column ensures a uniform format, which is necessary for downstream processing and feature generation, especially for time-series or temporal data.
- E. Incorrect.
Passing the raw dataset to the model without addressing inconsistencies would likely lead to errors or suboptimal model performance, as most models cannot handle such issues.