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Question Generator›Data Analyst

Free tool · no sign-up · 10 seconds

Free Data Analyst Interview Question Generator

Generate AI-powered Data Analyst interview questions instantly — technical, behavioral, and situational. Tailored for Indian campus placements and fresher hiring.

Generate DA questions freeBrowse Data Analyst question bank

How to generate Data Analyst interview questions

  1. 1

    Enter your role

    Type or select your target role in the question generator. You can also specify experience level and domain for more tailored output.

  2. 2

    Generate questions

    Click "Generate questions" to get 10 curated interview questions in under 10 seconds — no account or sign-up needed.

  3. 3

    Practice your answers

    Work through each question aloud or in writing. Use the STAR method for behavioral questions and think through edge cases for technical questions.

  4. 4

    Upgrade for scored mock interviews

    For AI-scored practice with detailed feedback across 5 dimensions, start a full mock interview session on InterviewEra.

Sample Data Analyst interview questions

A preview from our curated question bank. The generator produces fresh, AI-tailored questions on each run.

  • 1

    Write a SQL query to find the second-highest salary from an Employee table.

    Tip: Use LIMIT with OFFSET, or a subquery with MAX(salary) WHERE salary < MAX. Mention edge cases: what if two employees share the highest salary?

  • 2

    What is the difference between INNER JOIN and LEFT JOIN?

    Tip: INNER JOIN returns only matching rows. LEFT JOIN returns all rows from the left table with NULLs for non-matching right rows. Draw a Venn diagram mentally as you explain.

  • 3

    What is the difference between GROUP BY and HAVING? Can you use HAVING without GROUP BY?

    Tip: GROUP BY groups rows; HAVING filters those groups. Yes, HAVING without GROUP BY treats the entire table as one group — rarely useful but valid SQL.

  • 4

    How do you handle missing values (NULLs) in a dataset? Give at least three strategies.

    Tip: Deletion (if <5% missing), mean/median/mode imputation, forward-fill for time series, or model-based imputation. State when you would choose each.

  • 5

    What is the difference between supervised and unsupervised learning?

    Tip: Supervised: labelled data, learns a mapping (classification/regression). Unsupervised: no labels, finds patterns (clustering, dimensionality reduction). Give one example each.

See all 10 curated Data Analyst questions →

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