DEA-C01 Question 342
Select 3An e-commerce company is analyzing customer reviews stored in an Amazon S3 bucket. These reviews contain inconsistent capitalization, unnecessary whitespace, and special characters that do not add value to the analysis. The company uses AWS Glue for ETL processing and plans to clean the data before performing sentiment analysis using Amazon Comprehend. Which cleansing techniques should be applied to ensure that the data is prepared effectively for analysis?
- A
Convert all text to lowercase
- B
Remove special characters that do not contribute to the analysis
- C
Replace missing values in customer reviews with a default value
- D
Trim leading and trailing whitespace
- E
Sort the reviews alphabetically
Show answer and explanation
Correct answers: A, B, D
Explanation
Data cleansing is a critical step in preparing textual data for analysis. Inconsistent capitalization, special characters, and unnecessary whitespace can introduce noise and reduce the quality of results. Converting text to lowercase, removing irrelevant special characters, and trimming whitespace are effective techniques for standardizing and cleaning the data. Replacing missing values and sorting data may be useful in other contexts but do not address the specific issues in this scenario.
- A. Correct.
Converting all text to lowercase ensures uniformity in the data, which is critical when performing text analysis or comparisons.
- B. Correct.
Removing special characters that do not contribute to the analysis helps reduce noise in the data and improves the quality of insights.
- C. Incorrect.
Replacing missing values is important in numerical datasets but is not applicable for text fields like customer reviews.
- D. Correct.
Trimming leading and trailing whitespace ensures that the data is clean and avoids unnecessary mismatches during analysis.
- E. Incorrect.
Sorting the reviews alphabetically does not contribute to data cleansing or preparation for sentiment analysis.