Data Engineering Data Quality and Testing

Question 1

Which data quality dimension measures whether data values fall within expected ranges or conform to defined rules?

Accepted Answer

Validity

Answer

Validity checks whether data conforms to defined formats, ranges, and business rules — for example, age must be between 0 and 150.

Question 2

In dbt (data build tool), what type of test checks that a column contains no NULL values?

Accepted Answer

not_null

Answer

The `not_null` test in dbt asserts that a specified column has no NULL values in the dataset.

Question 3

What is a 'data contract' in modern data engineering?

Accepted Answer

A formal agreement defining data schema, quality, and SLAs between data producers and consumers

Answer

A data contract is a formal specification that defines what data a producer will deliver, including schema, quality guarantees, and update frequency.

Question 4

Which testing approach validates the statistical properties and distributions of data rather than individual row correctness?

Accepted Answer

Statistical/distribution testing

Answer

Statistical testing checks that data distributions, mean values, standard deviations, and outlier rates remain within expected bounds.

Question 5

What does 'data freshness' measure in data quality monitoring?

Accepted Answer

How recently the data was updated relative to its expected update schedule

Answer

Data freshness measures whether data has been updated within its expected time window, detecting pipeline failures or delays.

Data Engineering Practice Test