FREE ADE Data Transformation & Cleaning Questions and Answers

Question 1

What is data transformation in pipelines?

Accepted Answer

Converting data format

Answer

Data transformation is a key stage in data pipelines where raw data is converted, cleaned, aggregated, and enriched to meet the requirements of the target system or analytical use case. This can involve changing data types, joining datasets, calculating new fields, or standardizing values. The goal is to make the data suitable for accurate and meaningful analysis.

Question 2

Which technique removes duplicate records?

Accepted Answer

Deduplication

Answer

Deduplication is a data quality technique used to identify and remove redundant copies of data within a dataset. Duplicate records can skew analytical results, waste storage space, and lead to inconsistencies. By ensuring that each unique entity or record is represented only once, deduplication improves data accuracy, integrity, and efficiency for downstream processes.

Question 3

What is data normalization?

Accepted Answer

Restructuring data to reduce redundancy

Answer

Data normalization is a database design technique used to organize tables in a relational database to minimize data redundancy and improve data integrity. It involves breaking down large tables into smaller, related tables and defining relationships between them. This structured approach ensures that data is stored logically, efficiently, and consistently, reducing update anomalies.

Question 4

Which method fills missing data values?

Accepted Answer

Imputation

Answer

Imputation is a data preprocessing technique used to fill in missing data values with substituted values. Various methods exist, such as mean, median, mode imputation, or more advanced statistical models, to estimate and replace the missing entries. This ensures that datasets are complete and can be used for analysis without discarding valuable records, which might otherwise lead to biased results.

Question 5

What is the goal of data cleansing?

Accepted Answer

To correct errors

Answer

Data cleansing, also known as data scrubbing, is the process of detecting and correcting or removing corrupt, inaccurate, or irrelevant records from a dataset. Its primary goal is to improve data quality by ensuring data is accurate, consistent, and complete. This is vital for reliable analysis, accurate reporting, and sound decision-making, as flawed data can lead to incorrect insights.

ADE - Associate Data Engineer Certification Practice Test

ADE - Associate Data Engineer Certification Practice Test

FREE ADE Data Transformation & Cleaning Questions and Answers