DEA-C01 Question 345
Select 3You are working as a Data Engineer for a company that collects user clickstream data from its website. The data is stored in an Amazon S3 bucket and will be processed using AWS Glue and Amazon Redshift for analytics. However, the raw data contains issues such as duplicate records, null values in critical fields, and inconsistent date formats. Which cleansing techniques should you apply to ensure the data is ready for analysis?
- A
Remove duplicate records from the dataset.
- B
Fill null values in non-critical fields with default values.
- C
Standardize the date formats to a consistent format.
- D
Leave null values in critical fields unchanged to preserve raw data integrity.
- E
Remove rows with null values in critical fields.
Show answer and explanation
Correct answers: A, C, E
Explanation
In this scenario, cleansing techniques like removing duplicate records, standardizing date formats, and handling null values in critical fields are essential to prepare the dataset for analysis. Duplicate records can distort analytics, inconsistent date formats can cause processing issues, and null values in critical fields need to be addressed to maintain data quality. Filling null values in non-critical fields is not directly relevant to the problem stated, and leaving null values in critical fields can lead to errors.
- A. Correct.
Correct: Removing duplicate records is a fundamental data cleansing technique to ensure that the dataset is accurate and does not contain redundant information.
- B. Incorrect.
Incorrect: While filling null values in non-critical fields can be useful in some contexts, it is not explicitly stated that this is needed for the current scenario. The question focuses on critical fields and data consistency.
- C. Correct.
Correct: Standardizing date formats ensures consistency in the data, which is important for downstream analytics and transformations.
- D. Incorrect.
Incorrect: Leaving null values in critical fields unchanged can lead to data quality issues and analysis errors. These fields need to be addressed.
- E. Correct.
Correct: Removing rows with null values in critical fields is a valid cleansing technique when such fields are essential for analytics, as missing critical data can skew results.