Large Dataset Management and Technical Analysis Questions

Develop skills in working efficiently with large datasets: data cleaning and validation, efficient aggregation and manipulation, handling missing data, identifying and managing outliers. Master advanced Excel features or learn SQL for database queries. Practice data quality assessment. Learn efficient workflows that scale with dataset size. Understand data security and privacy considerations.

HardTechnical

39 practiced

Explain probabilistic data structures — HyperLogLog, Bloom filter, and Count-Min Sketch — and when each is appropriate for BI tasks at scale. Provide an example architecture using HyperLogLog to approximate unique daily active users across 100M events, and discuss mergeability, error bounds, and storage trade-offs.

MediumTechnical

73 practiced

Explain SCD Type 1, Type 2, and Type 3 strategies for handling changing dimensions in BI. For a customer dimension where addresses change frequently but you do not want to create a new historical row for minor corrections, which SCD strategy would you use and how would you implement corrections to past rows without breaking historical reporting?

MediumTechnical

46 practiced

Write a SQL query (Postgres or Snowflake) that computes a running total of revenue per user ordered by date but resets at the start of each month. Table:

transactions(user_id INT, occurred_at DATE, amount NUMERIC)

Explain the partitioning and frame clause you used and discuss performance considerations when running this on 200M rows.

EasyTechnical

40 practiced

Given a large PostgreSQL transactions table with schema:

transactions(
  transaction_id BIGINT PRIMARY KEY,
  user_id INT,
  amount DECIMAL,
  occurred_at TIMESTAMP,
  status VARCHAR
)

Write a PostgreSQL query that returns, for each of the last 12 full months, the month, total transaction amount, and average transaction amount per active user (exclude NULL amounts and status = 'cancelled'). Explain how you would include months with zero transactions so the result shows continuous monthly rows for reporting.

HardTechnical

40 practiced

You must ensure BI dashboards comply with GDPR when they surface personal data. Describe an end-to-end strategy covering data minimization, consent mapping, pseudonymization/anonymization, role-based access, retention policies, right-to-be-forgotten workflows, and audit trails. Explain how to design dashboards that show aggregates while avoiding re-identification.

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