Data validation testing questions come up in ETL testing, data quality, database testing and data analyst interviews. These 30 questions cover the concepts, the SQL and the scenarios interviewers use.
Basics (Q1–Q8)
1. What is data validation testing?
Verifying that data is accurate, complete, consistent and in the expected format as it is captured, moved and transformed.
2. Data validation vs data verification?
Validation checks data meets rules and is fit for use; verification checks data was copied or moved correctly from its source.
3. Where is data validated in a pipeline?
At the source (profiling), after extraction (staging), after transformation (target) and at reports.
4. What are data quality dimensions?
Completeness, validity, accuracy, consistency, uniqueness and timeliness. See data quality checks.
5. What is field-level validation?
Checking each column against type, length, format, range and allowed-value rules.
6. What is record-level validation?
Checking whole records: required combinations, cross-field rules and uniqueness.
7. What is referential validation?
Checking foreign keys have matching parent records.
8. What is reconciliation?
Comparing counts, totals and records between source and target. See data reconciliation.
Techniques and SQL (Q9–Q18)
9. How do you check completeness?
Compare counts and use EXCEPT/MINUS on keys to find missing records.
10. How do you check validity of formats?
Use LIKE or regular expressions, LENGTH and CASE rules, for example 6-digit PIN codes or valid email patterns.
11. How do you check ranges?
WHERE amount < 0 OR age NOT BETWEEN 18 AND 100, expecting zero rows.
12. How do you check allowed values?
WHERE status NOT IN (…) or a LEFT JOIN to a reference table.
13. How do you check uniqueness?
GROUP BY key HAVING COUNT(*) > 1.
-- Q14: invalid emails and 6-digit PIN code check (PostgreSQL regex syntax) SELECT COUNT(*) FROM customers WHERE email !~ '^[^@\s]+@[^@\s]+\.[^@\s]+$' OR pin_code !~ '^[1-9][0-9]{5}$';
14. How do you validate formats with regular expressions?
Use the database's regex operator (as above) or Python, keeping patterns simple and documented.
15. How do you validate a transformation?
Recompute the rule from source columns and compare with the target column.
16. How do you validate data across systems?
Extract keys and values from both, then compare in a common database or in Python.
17. What is data profiling?
Analysing data to understand nulls, distinct values, ranges and patterns before defining rules. See data profiling.
18. How do you validate large tables efficiently?
Start with counts, sums and hashes per partition, then drill down only where they differ.
Tools and Automation (Q19–Q24)
19. Which tools are used for data validation?
SQL, Python (pandas, Great Expectations, datacompy), dbt tests, QuerySurge and cloud features like AWS Glue Data Quality.
20. How do you automate data validation?
Store rules as SQL or code, run them after every load from a scheduler or CI, write results to a table and alert on failures.
21. What is Great Expectations?
An open-source Python framework for declaring and running data quality expectations. Tutorial.
22. What are dbt tests?
Tests such as unique, not_null, accepted_values and relationships defined on dbt models. Tutorial.
23. How do you report validation results?
A summary per table and rule with pass/fail, failing row counts and sample rows, plus a trend dashboard.
24. What thresholds do you use?
Zero tolerance for keys and money; agreed small tolerances for non-critical fields, documented with the business.
Scenario Questions (Q25–Q30)
25. Counts match but totals do not. What next?
Compare totals by a business dimension to narrow the gap, then compare records field by field in that group.
26. 5% of phone numbers are invalid. Is it a defect?
It depends on the rule: if the source has them, it is a source data issue to report; if the ETL corrupted them, it is an ETL defect.
27. A validation job takes too long. How do you speed it up?
Push aggregates to the database, validate by partition, use hashes and only compare details where summaries differ.
28. How do you validate a migrated table?
Counts, totals, missing records and field-level comparisons on critical columns. See data migration testing.
29. Source has NULLs in a mandatory column. What do you do?
Check the mapping: reject, default or load as unknown, and confirm the ETL follows that rule.
30. How do you prove data in a report is correct?
Rebuild the report numbers with SQL on the warehouse using the same filters and compare.
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Frequently Asked Questions
What is data validation testing?
It is testing that data is accurate, complete, consistent and correctly formatted as it is captured, moved and transformed, usually with SQL rules.
What questions are asked in data validation interviews?
Definitions, data quality dimensions, SQL for completeness, uniqueness, ranges and formats, tools such as Great Expectations and dbt, automation and real scenarios.
What is the difference between data validation and data verification?
Validation checks data meets business rules; verification checks data was transferred correctly from its source.
Which tools are used for data validation testing?
SQL, Python with pandas and Great Expectations, dbt tests, QuerySurge and cloud data quality features.
Is data validation testing the same as ETL testing?
It is a core part of ETL testing. ETL testing also covers process aspects such as load scheduling, incremental logic and performance.
Asim Noaman Lodhi
Certified Google Partner · QA Consultant · 12+ Years IT
QA consultant specializing in ETL testing and data quality. Trained 913+ students to transition into data testing roles through hands-on, real-world instruction.