Provyn Prep · Data Analyst competency
Data quality & definitions in the Data Analyst assessment
Data quality & definitions is one of the areas a Provyn Data Analyst credential scores separately, so a recruiter can see it on its own rather than averaged into one number. This guide explains what it covers, how it is tested and what to practice.
What it covers
Checking data before trusting it: NULLs, numeric types and rounding, duplicates and consistent definitions.
Topics in the practice set for this area
- numeric types
- null handling
How it is tested
In the timed assessment, questions in this area use these formats: code you write, multiple choice, short answer. Each attempt draws its own set of questions, and this area gets its own sub-score on the credential when the attempt includes enough questions from it. The assessment questions themselves are never published; the practice set below is written separately for study.
What to practice
- Write checks for NULLs, duplicates and out-of-range values on a new table.
- Show how integer division or rounding can change a reported rate.
- Document the definition of one metric so it can be reproduced exactly.
Try a practice question
From the free practice set (2 questions in this area). Answers and explanations are in the practice set.
- In Postgres, `converted` and `visitors` are INTEGER columns with values 7 and 20. What does `SELECT converted / visitors` return?
- A click-through-rate query `clicks * 1.0 / impressions` fails on days when an ad had 0 impressions. Which change returns NULL for those days instead of an error?
Practice before the real thing
Both are free and use practice questions only, never questions from the real assessment.
More Data Analyst study guides
Last reviewed 2026-10-06.