AWS S3 Scenario Based Interview Questions

Last updated on March 8th, 2025 at 01:13 pm

Here, we will discuss AWS S3 Scenario Based Interview Questions, which interviewers ask in most company interviews for mainly AWS Data Engineer job positions.

1. What is AWS S3?

AWS S3 (Simple Storage Service) is a scalable object storage service for data archiving, backup, and analytics. It offers 99.999999999% durability, unlimited storage, and integration with AWS services.

2. AWS S3 Interview Topics

Storage Classes: Standard, IA, Glacier (cost vs. access frequency).
Security: Encryption (SSE-S3, SSE-KMS, SSE-C), bucket policies, ACLs.
Versioning: Track/restore object versions.
Replication: Cross-region (CRR) for disaster recovery.
Lifecycle Policies: Automate tier transitions/expiry.
Performance: Multipart uploads, Transfer Acceleration, request rate optimization.
Consistency Model: Strong consistency for PUTs/DELETEs (post-2020).
Access Control: IAM roles, presigned URLs.
Data Lake Integration: Athena, Redshift, Lake Formation.
Cost Optimization: Intelligent-Tiering, S3 Select (query filtering).

1. You have an AWS Databricks job processing a large dataset stored in S3 that is running slower than expected. What are the possible reasons, and how would you troubleshoot and optimize the job?

Slow AWS Databricks Job Processing: A slow Databricks job processing S3 data often stems from inefficient data partitioning, insufficient cluster resources, or data skew.

To troubleshoot – review job execution plans, analyze Spark UI metrics, and optimize data partitions.

Start by analyzing Spark UI logs to identify long-running stages or skewed partitions.
Check if S3 data is partitioned optimally (e.g., by date/region) to reduce scan volume.
Use larger instances or enable autoscaling to address resource bottlenecks.
For data skew, repartition skewed keys or use salting.
Optimize caching for reused datasets and enable Delta Lake’s Z-ordering for faster queries.
Using caching and optimizing the data storage format (e.g., using Parquet or Delta).

2. You need to process both streaming and batch data in AWS Databricks. How would you design a solution to ensure both workloads run efficiently without resource contention?

Streaming + Batch Workload Design: The below steps ensure that both workloads streaming and batch data run efficiently without resource contention:

Use isolated clusters for streaming and batch jobs to avoid resource contention.
For streaming, leverage Delta Lake’s ACID transactions and structured streaming with checkpoints.
For batch, scheduled during off-peak hours.
Utilize instance pools to reduce startup delays.
Enable cluster autoscaling with spot instances for cost efficiency.
Use Spark’s Fair Scheduler to prioritize streaming tasks if shared clusters are unavoidable.

3. You notice a significant performance drop when using joins in a Databricks notebook. What strategies can you apply to optimize joins in large datasets?

Optimizing Joins in Databricks: Strategies applied to optimize joins in larger datasets are:

Improve join performance by broadcasting smaller tables (if under 10MB), bucketing datasets on join keys, and denormalizing where possible.
Avoid cross-joins; instead, pre-filter data.
Use Delta Lake’s optimize and Z-order to colocate join keys.
If skew exists, split skewed keys manually or use Spark’s skewjoin hint.
Monitor execution plans to eliminate unnecessary shuffles.

4. A team wants to use Delta Lake in AWS Databricks for their ETL jobs. What are the key challenges in managing Delta Lake tables, and how would you ensure high performance and data consistency?

Delta Lake ETL Challenges: Key challenges include transaction log management, small file issues, and schema enforcement.

Use optimization to compact small files and Z-ordering for query acceleration.
Enable schema validation to prevent corruption.
Schedule a vacuum to manage retention but avoid over-aggressive cleanup.
For concurrency, use MERGE for upserts and isolate writes via table versioning.

5. How would you handle schema evolution when reading data from S3 using Delta Lake in AWS Databricks?

Schema Evolution with Delta Lake: The following step is used to handle schema evolution.

Enable automatic schema evolution using mergeSchema=True in Delta Lake.
For breaking changes (e.g., column renames), use ALTER TABLE to update schemas explicitly.
Use versioned tables to handle backward compatibility.
For streaming jobs, set allowSchemaLocation to manage evolving schemas in S3 without breaking pipelines.

6. Your Spark job running on AWS Databricks is getting OutOfMemory errors when processing a large dataset. What are the steps you would take to resolve this?

Resolving OutOfMemory Errors: The following steps are used to resolve OutOfMemory errors from AWS Databricks:

Scale vertically by increasing executor memory or horizontally by adding nodes.
Repartition data to reduce per-task memory pressure.
Avoid collect(); use take() or limit output.
Tune spark.sql.shuffle.partitions to balance parallelism.
Enable off-heap memory or switch to Kryo serialization.
For drivers, increase driver-memory and avoid broadcasting large variables.

7. How would you reduce costs while running large-scale jobs in AWS Databricks, especially when dealing with spot instances and on-demand instances?

Cost Reduction Strategies: The Following strategies are used to reduce costs for long-scale running jobs in AWS Databricks:

Combine spot instances (for resilience-tolerant tasks) with on-demand (for critical jobs).
Use autoscaling to minimize idle nodes.
Right-size clusters (e.g., memory-optimized for in-memory processing).
Terminate clusters post-job via APIs.
Enable Delta Cache to reduce S3 API calls.
Analyze usage with Databricks Cost Insights to identify waste.

8. You’ve been asked to optimize a long-running AWS Databricks job that processes terabytes of data. How would you approach this task, focusing on both performance and cost?

Optimizing Long-Running Jobs: The Following strategies are used to optimize long-scale running jobs in AWS Databricks:

Profile the job using Spark UI to spot bottlenecks like excessive shuffles.
Use Delta Lake for efficient file management.
Cache intermediate data if reused.
Optimize queries with predicate pushdown and partition pruning.
Tune spark.default.parallelism to match data size.
For cost, use spot instances and photon acceleration for CPU-heavy tasks.

9. How would you configure AWS Databricks Auto-scaling for optimal performance, and when would you use a standard vs. a high-concurrency cluster?

Auto-Scaling Configuration: The Following steps for auto-scale configuration in AWS Databricks for optimal performance:

Use standard clusters for single-user jobs requiring full resource control.
High-concurrency clusters (with UC) are ideal for multi-user SQL/BI workloads.
Configure auto-scaling with a 2-5x worker range.
Set aggressive downscaling (e.g., 10-minute inactivity) for batch jobs but avoid thrashing.
For streaming, set minimum workers to sustain throughput.

10. You have a requirement to process IoT sensor data in real time using AWS Databricks. How would you design and optimize a real-time pipeline that ingests data from Kinesis or Kafka?

Real-Time IoT Pipeline Design: Requirement needs to design an IoT pipeline that ingests data from Kinesis or Kafka in real-time:

Ingest data via Kinesis/Kafka into Delta Lake using Structured Streaming.
Use watermarking for late data and foreachBatch for complex writes.
Optimize with Delta’s MERGE for upserts.
Scale receivers by partitioning streams by device ID.
Use Delta Live Tables for declarative pipeline management.
For low latency, tune trigger intervals (e.g., 1s) and use i3en instances for high IOPS.

Table of Contents

1. What is AWS S3?

2. AWS S3 Interview Topics