Focuses on combining datasets correctly and efficiently. Includes different join types such as inner, left, right, full outer, and cross joins; implications of each join type for result cardinality and missing data; strategies for resolving many to many relationships and duplicate records; methods for identifying and cleaning and aligning join keys including normalization and fuzzy matching; handling mismatched or missing keys and null semantics; performance and memory considerations when joining large tables or distributed datasets; and testing and validation to ensure joins preserve referential integrity and do not introduce inadvertent data leakage.
EasyTechnical
0 practiced
Describe how NULL values in join keys are treated by SQL joins (inner, left, right, full) and by pandas merging. Specifically explain whether NULLs match each other, how that affects record counts after joins, and practical approaches to handle NULLs in join keys before merging datasets for analysis.
HardTechnical
0 practiced
After several joins, a cohort's feature distributions changed unexpectedly. Propose an approach to statistically detect whether distribution shifts are due to join mismatches (e.g., lost rows, duplicated keys) versus real upstream data changes. Include metrics, delta tests, and tooling you would use.
EasyTechnical
0 practiced
In Python using pandas, given two DataFrames users(user_id, name, signup_date) and events(event_id, user_id, event_ts), show how to perform a left join to attach the most recent event timestamp to each user. Provide code that handles users with no events by keeping NaN and explain how merge parameters (how, on, validate) influence results and help catch unexpected multiplicity.
HardSystem Design
0 practiced
Design a robust streaming join between an event stream and a slowly changing dimension in a stream processing system (e.g., Flink or Kafka Streams). Address out-of-order events, state management, windowing semantics, TTL for state, and how to provide exactly-once semantics with joins.
EasyTechnical
0 practiced
Explain the differences between inner join, left join, right join, full outer join, and cross join in relational databases. For each join type, describe the expected result cardinality relative to input tables, typical use cases a data scientist might have when preparing features, and one concrete example where choosing the wrong join type could produce incorrect training data.
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