400 Data Warehouse Interview Questions with Answers 2026

ETL/ELT & Integration: CDC, Idempotency, Backfilling, Staging, JSON/Parquet handling, API Integration.

Performance & Optimization: Indexing vs. Partitioning, Materialized Views, Distribution Keys, MPP, Execution Plans.

Cloud Warehousing & Tooling: Snowflake/BigQuery/Redshift, Data Mesh vs. Fabric, Lakehouses, dbt, CI/CD.

Governance & Security: Data Lineage, PII Masking, RBAC, Data Quality SLAs, GDPR/SOC2 Compliance.

B) It allows a job to be re-run multiple times with the same input producing the same output without duplicating or corrupting data.

C) It automatically converts row-based storage into columnar format for better compression.

D) It guarantees that the data satisfies third-normal form (3NF) requirements before entering the warehouse.

E) It limits the compute cost by preventing the query engine from scaling horizontally.

F) It is a requirement for maintaining SOC2 compliance in financial reporting.

Correct Answer: B

Overall Explanation: Idempotency is a functional requirement in data engineering where an operation can be applied multiple times without changing the result beyond the initial application. This is vital for fault tolerance and retrying failed pipeline runs.

Detailed Option Analysis:

B: Correct; this is the definition of idempotency, ensuring consistency during retries.

C: Incorrect; this is a storage optimization feature, usually handled by the file format (e.g., Parquet).

D: Incorrect; normalization is a modeling choice, not a pipeline property.

E: Incorrect; idempotency does not restrict compute scaling.

F: Incorrect; while good practice, it is not a direct legal requirement of SOC2.

Question 2: Which Slowly Changing Dimension (SCD) type would you implement if the business requirement demands tracking the full history of changes while also providing a "current flag" and an "effective date" for easy filtering?

B) Type 1

C) Type 2

D) Type 3

E) Type 4

F) Type 6

Correct Answer: C

Overall Explanation: SCD Type 2 is the industry standard for tracking historical data by creating new rows for each change, using metadata columns to identify current versus historical records.

Detailed Option Analysis:

B: Incorrect; Type 1 overwrites data, losing all history.

C: Correct; Type 2 uses surrogate keys and versioning (dates/flags) to track full history.

D: Incorrect; Type 3 only tracks the "previous" and "current" values in separate columns.

E: Incorrect; Type 4 uses a separate history table rather than flags in the main dimension.

F: Incorrect; Type 6 (2+3+1) is a hybrid approach and is often overkill for simple history tracking.

Question 3: In a Massively Parallel Processing (MPP) architecture like Amazon Redshift or Azure Synapse, what is the primary risk of choosing a distribution key with low cardinality for a large fact table?

B) It forces the system to use a Star Schema instead of a Snowflake Schema.

C) It automatically triggers a full table vacuum after every insert.

D) It increases the cost of storage by duplicating data across all nodes.

E) It prevents the use of Materialized Views on that specific table.

F) It disables the ability to use Role-Based Access Control (RBAC).

Correct Answer: A

Overall Explanation: MPP systems distribute data across multiple nodes. If a distribution key has few unique values (low cardinality), the data cannot be spread evenly, causing "hot spots" or skew that slows down the entire cluster.

Detailed Option Analysis:

B: Incorrect; distribution keys are independent of the logical schema design.

C: Incorrect; vacuuming is a maintenance task unrelated to distribution key cardinality.

D: Incorrect; this describes "All" or "Broadcast" distribution, not a specific key risk.

E: Incorrect; Materialized Views can still be used, though they may also suffer from skew.

F: Incorrect; security (RBAC) is managed at the metadata layer, not the physical distribution layer.

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