TikTok·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

TikTok data scientist interview with a SQL question focused on random sampling. Pretty straightforward if you know your database functions, but I second-guessed myself more than I should have.

Questions Asked (1)

Write a SQL query that randomly selects roughly 10% of rows from a large user table.

Product Analytics & MetricsAlgorithms & Data Structures

Author's notes

I went with WHERE RAND() < 0.1 which works fine, but then spent like two minutes nervously over-explaining why it's approximate and not exact.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the database system (e.g., PostgreSQL, MySQL, BigQuery) and the definition of 'randomly' and 'roughly 10%'. Then propose a sampling method that balances randomness with performance on large tables, such as using a hash of a unique key or TABLESAMPLE if supported.

Pro tip: For very large tables, avoid ORDER BY RAND() as it requires a full sort; instead use a deterministic hash-based approach that can leverage indexes or partitions. Also mention that 'roughly' allows for statistical sampling, which is often more efficient than exact 10%.

1. Clarify requirements and constraints

Ask about the database system, table size, and whether the sample needs to be exactly 10% or approximately 10%. Confirm if randomness should be uniform across all rows.

2. Choose a sampling method

Select an appropriate technique: TABLESAMPLE (if available), hash-based sampling (e.g., MOD(ABS(HASH(user_id)), 10) = 0), or random() < 0.1. Consider performance and randomness quality.

3. Write the SQL query

Construct the query using the chosen method, ensuring it is efficient and correct. For example: SELECT * FROM users WHERE MOD(ABS(HASH(user_id)), 10) = 0; or SELECT * FROM users TABLESAMPLE BERNOULLI(10);

4. Discuss trade-offs and alternatives

Explain the pros and cons of your approach: hash-based is deterministic and fast but may have slight bias; random() is simple but slow on large tables; TABLESAMPLE is fast but may not be supported everywhere.

5. Validate and optimize

Mention how to verify the sample size (e.g., COUNT(*)) and suggest optimizations like using a subquery or leveraging partitioning if the table is partitioned.

Key Points to Mention

Database-specific functions: TABLESAMPLE (PostgreSQL, BigQuery), SAMPLE (Snowflake), or RAND() (MySQL).
Hash-based sampling using MOD and HASH functions for deterministic and efficient sampling.
Performance considerations: avoiding full table scans and sorting (e.g., ORDER BY RAND() is inefficient).
Statistical validity: ensuring uniform randomness and independence of selection.
Handling large tables: using partitioning, clustering, or approximate methods.
Edge cases: small tables, empty tables, or when exact 10% is required.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.