DEA-C01 Question 343
Select 3A company is ingesting customer purchase data from multiple sources into an Amazon S3 bucket for downstream analytics. However, the data contains missing values, duplicate rows, and inconsistent date formats. What are the appropriate cleansing techniques to apply to ensure the data is ready for analysis?
- A
Remove duplicate rows to ensure data integrity.
- B
Replace missing values with default or estimated values.
- C
Normalize the date formats to a consistent format.
- D
Delete all rows with missing values to avoid inaccuracies.
- E
Apply tokenization for sensitive fields like credit card numbers.
Show answer and explanation
Correct answers: A, B, C
Explanation
Data cleansing involves techniques like removing duplicates, handling missing values, and normalizing formats to ensure the dataset is accurate, consistent, and ready for analysis. Removing duplicates ensures no redundancy, while handling missing values and normalizing date formats maintain data quality. Excessive deletion of rows or applying unrelated techniques like tokenization are not suitable for this scenario.
- A. Correct.
Removing duplicate rows is a standard cleansing technique to ensure that the dataset does not contain redundant information that could skew analysis.
- B. Correct.
Replacing missing values with default or estimated values is a common practice to maintain data usability and avoid downstream errors in analysis.
- C. Correct.
Normalizing date formats ensures consistency in how dates are represented, which is crucial for accurate data processing and analysis.
- D. Incorrect.
Deleting all rows with missing values is not always appropriate as it could lead to significant data loss, especially when missing data is minimal and can be handled with other techniques.
- E. Incorrect.
Tokenization is not a data cleansing technique; it is a security practice to protect sensitive information and is unrelated to preparing data for analysis.