Data Quality and Testing Flashcards
6 cards from real Data Engineering practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Data Quality and Testing flashcards as text
Which data quality dimension measures whether data values fall within expected ranges or conform to defined rules?
Answer: Validity
Validity checks whether data conforms to defined formats, ranges, and business rules — for example, age must be between 0 and 150.
In dbt (data build tool), what type of test checks that a column contains no NULL values?
Answer: not_null
The `not_null` test in dbt asserts that a specified column has no NULL values in the dataset.
What is a 'data contract' in modern data engineering?
Answer: A formal agreement defining data schema, quality, and SLAs between data producers and consumers
A data contract is a formal specification that defines what data a producer will deliver, including schema, quality guarantees, and update frequency.
Which testing approach validates the statistical properties and distributions of data rather than individual row correctness?
Answer: Statistical/distribution testing
Statistical testing checks that data distributions, mean values, standard deviations, and outlier rates remain within expected bounds.
What does 'data freshness' measure in data quality monitoring?
Answer: How recently the data was updated relative to its expected update schedule
Data freshness measures whether data has been updated within its expected time window, detecting pipeline failures or delays.
In Great Expectations, what is an 'Expectation Suite'?
Answer: A collection of individual expectations that define what valid data looks like
An Expectation Suite is a named collection of expectations that together define the complete quality specification for a dataset.