Preparing for a Databricks RSA (Resident Solutions Architect) interview requires more than just knowing Spark APIs. You should understand architecture, security, governance, optimization, and customer best practices.

This guide covers commonly asked RSA interview questions with practical answers.

1. What is Databricks?

Databricks is a unified analytics platform built on Apache Spark that enables data engineering, data science, machine learning, data warehousing, and AI workloads from a single workspace.

Key Features

• Apache Spark-based platform
• Lakehouse Architecture
• Delta Lake
• Unity Catalog
• Databricks SQL
• MLflow integration
• Workflows
• AI and GenAI capabilities

2. What is the Databricks Lakehouse Architecture?

Lakehouse combines the advantages of a Data Lake and a Data Warehouse.

Data Lake Benefits

• Low-cost storage
• Supports structured and unstructured data
• Scalable

Data Warehouse Benefits

• ACID transactions
• Schema enforcement
• High-performance SQL
• Governance

The Lakehouse provides both capabilities in one architecture.

3. What is Delta Lake?

Delta Lake is an open-source storage layer that adds reliability to data lakes.

Features

• ACID Transactions
• Schema Enforcement
• Schema Evolution
• Time Travel
• Change Data Feed (CDF)
• MERGE
• DELETE
• UPDATE

Example:

MERGE INTO customer t
USING updates s
ON t.id = s.id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *

4. What is Time Travel?

Time Travel allows users to query previous versions of a Delta table.

Example:

SELECT *
FROM sales VERSION AS OF 5;

or

SELECT *
FROM sales TIMESTAMP AS OF '2026-07-01';

Benefits

• Data recovery
• Auditing
• Debugging
• Reproducible analytics

5. What is Change Data Feed (CDF)?

CDF records row-level changes in Delta tables.

It tracks:

• Inserts
• Updates
• Deletes

Example:

SELECT *
FROM table_changes('customers',5,10);

Useful for:

• Incremental ETL
• CDC Pipelines
• Data synchronization

6. Explain Medallion Architecture.

The Medallion Architecture organizes data into layers.

Bronze

• Raw data
• Minimal transformations

Silver

• Cleansed
• Validated
• Joined
• Deduplicated

Gold

• Business-ready
• Aggregated
• Reporting datasets

7. What is Unity Catalog?

Unity Catalog is Databricks’ centralized governance solution.

Features include:

• Fine-grained permissions
• Row-level security
• Column-level security
• Lineage
• Audit logs
• Centralized metadata

8. Difference Between Hive Metastore and Unity Catalog

9. What is Photon?

Photon is Databricks’ native vectorized query engine.

Benefits:

• Faster SQL
• Lower cost
• Optimized joins
• Faster aggregations
• Better BI performance

10. What is Liquid Clustering?

Liquid Clustering automatically reorganizes table data without repartitioning.

Advantages:

• No partition management
• Better query performance
• Easier maintenance
• Adaptive optimization

11. Difference Between Partitioning and Liquid Clustering

12. What is Predictive Optimization?

Predictive Optimization automatically performs:

• OPTIMIZE
• VACUUM
• Statistics collection

Benefits:

• Less manual work
• Better query performance
• Lower operational overhead

13. Explain Auto Loader.

Auto Loader efficiently ingests files from cloud storage.

Supported Sources

• AWS S3
• Azure ADLS
• Google Cloud Storage

Features

• Incremental ingestion
• Schema inference
• Schema evolution
• Checkpointing

14. What is Structured Streaming?

Structured Streaming processes continuous data streams using the Spark DataFrame API.

Supports:

• Kafka
• Event Hubs
• Kinesis
• Files

Processing Modes

• Micro-batch
• Continuous

15. Difference Between Batch and Streaming

16. What is Adaptive Query Execution (AQE)?

AQE optimizes queries during execution.

Features:

• Dynamic join selection
• Skew join optimization
• Partition coalescing
• Better execution plans

17. Broadcast Join vs Shuffle Join

Broadcast Join

• Small table
• No shuffle
• Faster

Shuffle Join

• Large tables
• Data movement required
• Higher execution time

18. What causes Data Skew?

Common reasons:

• One key has significantly more records
• Uneven partition sizes
• Poor partitioning

Solutions:

• Salting
• AQE
• Repartitioning
• Broadcast joins

19. What is Z-Ordering?

Z-Ordering colocates related data to reduce the amount of data scanned.

Example:

OPTIMIZE sales
ZORDER BY (customer_id);

Benefits:

• Faster filtering
• Reduced IO
• Improved query performance

20. What is VACUUM?

VACUUM removes old files no longer referenced by a Delta table.

Example:

VACUUM sales RETAIN 168 HOURS;

21. Difference Between OPTIMIZE and VACUUM

22. What is Delta Live Tables (DLT)?

DLT simplifies building reliable ETL pipelines.

Features:

• Declarative pipelines
• Data quality expectations
• Automatic dependency management
• Monitoring
• Auto recovery

23. What are DLT Expectations?

Expectations validate incoming data.

Example:

@dlt.expect("valid_age","age > 18")

Actions

• Warn
• Drop
• Fail

24. What are Databricks Workflows?

Workflows orchestrate jobs.

Capabilities:

• Scheduling
• Task dependencies
• Retries
• Notifications
• Job clusters

25. How do you optimize a Databricks job?

Best practices include:

• Use Photon
• Enable AQE
• Cache reusable data
• Broadcast small tables
• Optimize file sizes
• Avoid unnecessary shuffles
• Use partition pruning
• Enable Liquid Clustering when appropriate

26. Explain Serverless Compute.

Serverless Compute removes cluster management.

Benefits:

• Instant startup
• Auto scaling
• Lower operational overhead
• Cost optimization

27. What is Delta Sharing?

Delta Sharing securely shares live data across organizations without copying data.

Supports:

• Open protocol
• Cross-cloud sharing
• External partners

28. What is Databricks Asset Bundles?

Asset Bundles package Databricks resources as code.

They include:

• Jobs
• Pipelines
• Notebooks
• Dashboards
• Permissions

Ideal for CI/CD and deployment automation.

29. What monitoring tools are available in Databricks?

Common monitoring options include:

• Spark UI
• Query History
• Ganglia (legacy clusters)
• Workflow run history
• Event logs
• System tables
• Unity Catalog lineage

These tools help identify bottlenecks, failed tasks, long-running queries, and resource utilization.

30. What qualities are expected from a Databricks RSA?

A successful RSA should be able to:

• Design scalable Lakehouse architectures.
• Advise customers on security and governance.
• Optimize workloads for performance and cost.
• Troubleshoot Spark, SQL, and pipeline issues.
• Guide customers on best practices for Unity Catalog, Delta Lake, and AI workloads.
• Communicate complex technical concepts clearly to both technical and business stakeholders.

Final Thoughts

A Databricks RSA interview evaluates both technical expertise and architectural thinking. Focus on understanding why a feature is used, not just how it works. Practice designing end-to-end Lakehouse solutions, explaining trade-offs, and discussing performance optimization, governance, and operational best practices.

Mastering topics such as Delta Lake, Unity Catalog, Workflows, Auto Loader, Structured Streaming, Photon, Liquid Clustering, and performance tuning will help you confidently answer most RSA interview questions.