400 Pyspark Interview Questions with Answers 2026

Performance Tuning: Data Skew (Salting), Broadcast Joins, Caching vs. Persisting, and Spark UI analysis.

Structured APIs: Window functions, Nested JSON/Parquet handling, and UDF optimization.

Data Governance & Security: RBAC, PII masking, ACID properties in Delta Lake, and Secret Management.

Streaming & Deployment: Watermarking, Checkpointing, Exactly-once semantics, and K8s vs. YARN.

B. Applying a .select() statement to rename multiple columns.

C. Performing a .groupBy() operation to aggregate sales by region.

D. Utilizing .map() to apply a Python function to every row.

E. Adding a new column using .withColumn() with a literal value.

F. Executing a .limit() operation on a small local dataset.

Correct Answer: C

Overall Explanation: Transformations in Spark are categorized as either Narrow (data stays within a partition) or Wide (data must be redistributed across the cluster). Wide transformations require a shuffle.

Option Explanations: * A (Incorrect): Filter is a narrow transformation; it happens locally within each partition.

C (Correct): GroupBy requires data with the same key to be moved to the same executor, triggering a shuffle.

D (Incorrect): Map operations are performed row-by-row within the same partition.

E (Incorrect): Adding a literal value does not require data movement between partitions.

F (Incorrect): While limit involves coordination, it is not fundamentally a wide transformation in the same way a shuffle-based aggregate is.

Question 2: You notice a "Data Skew" issue where one task takes significantly longer than others during a Join. Which technique is most effective for mitigating this in Spark 3.x?

B. Disabling the Catalyst Optimizer to manually reorder joins.

C. Implementing "Salting" by adding a random key to the join column.

D. Reducing the number of shuffle partitions to 10.

E. Using .coalesce(1) before the join operation.

F. Switching from a DataFrame API to the RDD API for the join.

Correct Answer: C

Overall Explanation: Data skew occurs when a specific key has significantly more records than others, overloading a single task. Salting redistributes these records more evenly.

Option Explanations:

B (Incorrect): Disabling the optimizer would likely decrease overall performance.

C (Correct): Salting breaks up the skewed key into smaller sub-keys, allowing multiple tasks to process the data in parallel.

D (Incorrect): Reducing partitions often makes skew worse by forcing more data into fewer tasks.

E (Incorrect): Coalesce(1) would force all data to one executor, creating a massive bottleneck.

F (Incorrect): RDD joins are generally less optimized than DataFrame joins.

Question 3: In Structured Streaming, what is the primary purpose of defining a "Watermark"?

B. To specify how long the engine should wait for late-arriving data before discarding it.

C. To automatically increase the number of executors during peak traffic.

D. To create a physical backup of the data in the Checkpoint directory.

E. To define the interval at which the streaming query triggers a new batch.

F. To convert a streaming DataFrame into a static DataFrame for unit testing.

Correct Answer: B

Overall Explanation: Watermarking is a threshold used in windowed aggregations to handle "late" data and manage state store cleanup.

Option Explanations:

B (Correct): Watermarks allow Spark to track the maximum event time seen and ignore data that arrives after the allowed delay.

C (Incorrect): This refers to dynamic allocation or autoscaling.

D (Incorrect): Checkpointing handles state recovery; watermarking handles event-time logic.

E (Incorrect): This describes the "Trigger" interval.

F (Incorrect): Watermarking is a runtime logic for stream processing, not a type conversion tool.

This is a huge original question bank

You get support from instructors if you have questions

Each question has a detailed explanation

Mobile-compatible with the Udemy app

30-day money-back guarantee if you're not satisfied