Question 1

What is a Common Table Expression (CTE), and when would you use it?

Accepted Answer

**CTE**: A named temporary result set in a WITH clause, referenced in the main query. **Use cases**: Readability—break complex queries into steps. Reusability—reference same CTE multiple times. Recursion—hierarchies (org chart, bills of materials). **Why it matters**: CTEs improve maintainability; deep subqueries are hard to debug. **Scalability**: In some engines (e.g., PostgreSQL), CTEs are optimization fences—materialized once. In others (Snowflake, BigQuery), they're inlined....

Question 2

What is the difference between a primary key and a unique key?

Accepted Answer

**Primary Key**: Unique identifier; NOT NULL; one per table; often clustered. **Unique Key**: Enforces uniqueness; can have NULL (SQL allows one NULL per column in uniqueness); multiple per table. **Why it matters**: PK defines identity and referential integrity; unique constrains alternate keys (e.g., email). **Scalability**: PK is often the clustering key; choice affects physical layout. Unique indexes enable lookups. **Cost**: Each constraint adds index overhead....

Question 3

Explain Fact and Dimension Tables with examples.

Accepted Answer

Architecture: Star schema centralizes measurable events in fact tables; dimensions provide semantic context. Why this design: Facts are append-heavy and grow unbounded; dimensions are smaller and change slowly. Separating them optimizes for different access patterns. Fact grain defines the entire schema—get it wrong and joins become wrong. Example: sales_fact (quantity, revenue, date_key, product_key, customer_key) at grain one row per transaction....

Question 4

Joins and window functions - INNER, LEFT, RIGHT, FULL OUTER, ROW_NUMBER(), RANK(), DENSE_RANK()

Accepted Answer

Joins: INNER = intersection only; LEFT = all left + matching right (NULL fill); RIGHT = mirror of LEFT; FULL OUTER = union of both. Why it matters: Join choice affects result cardinality and NULL handling—wrong join = wrong business logic (e.g., LEFT to preserve all customers even without orders). Window functions: ROW_NUMBER() = unique rank 1,2,3; RANK() = ties same rank, gaps after; DENSE_RANK() = ties same rank, no gaps....

Question 5

Difference Between Internal and External Tables in BigQuery

Accepted Answer

**Architectural Logic**: Internal (native) tables: BigQuery owns metadata and storage (Colossus). Columnar layout, partitioning, clustering, automatic optimization. Dropping = data deleted. External tables: BigQuery owns metadata only; data lives in GCS, Drive, Bigtable. Dropping = metadata deleted, data persists. **Why**: Internal enables DML, streaming inserts, partitioning strategies, cost-effective storage classes....

Question 6

How do you optimize a long-running SQL query?

Accepted Answer

**Architectural Logic**: Optimization is diagnostic-first. 1. Profile: EXPLAIN/EXPLAIN ANALYZE to find bottleneck (scan, join, sort, spill). 2. Reduce input: Filter early (WHERE, partition pruning); SELECT only needed columns. 3. Indexing: B-tree on filter/join columns; avoid over-indexing (writes slow). 4. Partitioning: Date/tenant partitioning for pruning. 5. Join strategy: Broadcast small dims; avoid cross joins. 6. Statistics: Up-to-date stats for planner. 7....

Question 7

Cloud Architecture - explain

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 8

Consolidate hotel reviews and create a dashboard. Design a data model for the reviews.

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 9

Create Spark Session, read CSV, join, and write as table. Provide example code.

Accepted Answer

**Architectural Logic**: Production Spark patterns require config, partitioning, and join optimization.

```python
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("ETL")\
  .config("spark.sql.adaptive.enabled", "true")\
  .getOrCreate()
df1 = spark.read.option("header", True).csv("s3://bucket/orders.csv")
df2 = spark.read.option("header", True).csv("s3://bucket/customers.csv")
joined = df1.join(broadcast(df2), df1.customer_id == df2.id, "left")
joined.write.partitionBy(...

Question 10

Data Warehouse Design from scratch

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Hard SQL Interview Questions

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Hard SQL Interview Questions

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