---
title: "Top Databricks RSA Interview Questions on Delta & Lakehouse"
description: "Meta Description: Prepare for Databricks RSA interviews with these frequently asked questions and detailed answers covering Delta Lake, Unity Catalog, Spark, SQL, Performance Tuning, Workflows, and more."
url: https://srinimf.com/2026/07/12/databricks-rsa-preparation-top-interview-questions-and-answers-2026-guide/
date: 2026-07-12
modified: 2026-10-10
author: "Srini"
image: https://i0.wp.com/srinimf.com/wp-content/uploads/2026/07/pexels-photo-6248959.jpeg?fit=1880%2C1255&ssl=1
categories: ["databricks"]
tags: ["Databricks Interview", "Databricks RSA", "Databricks RSA Preparation", "Databricks Workflows"]
type: post
lang: en
---

# Top Databricks RSA Interview Questions on Delta & Lakehouse

Preparing for a Databricks RSA Interview requires more than just knowing Spark APIs; you also need to understand architecture, security, governance, optimization, and customer best practices, all of which this post will cover. If you find this guide useful, check out our [Books](/books/) and [E-Books](/e-books/) pages for more learning resources.

This guide covers commonly asked RSA [interview questions](https://srinimf.com/2023/05/06/10-must-read-interview-questions-on-scikit-learn/) with practical answers.

---

## 1. What is Databricks?

[Databricks](https://www.databricks.com/product/data-lakehouse) 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 of Databricks

- Apache Spark-based platform
- Lakehouse Architecture
- [Delta Lake](https://srinimf.com/2025/11/08/exploring-the-latest-delta-lake-features-in-databricks/)
- [Unity Catalog](https://srinimf.com/2025/11/21/unity-catalog-in-databricks-key-multiple-choice-questions/)
- Databricks SQL
- MLflow integration
- Workflows
- AI and GenAI capabilities

---

## 2. What is Lakehouse Architecture?

[Lakehouse](https://www.databricks.com/glossary/data-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](https://docs.databricks.com/en/delta/index.html) is an open-source storage layer that adds reliability to [data lakes](https://srinimf.com/2024/09/26/data-lake-vs-delta-lake-vs-lakehouse-explained/).

### Standout Features

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

Example:

```
MERGE INTO customer tUSING updates sON t.id = s.idWHEN MATCHED THEN UPDATE SET *WHEN NOT MATCHED THEN INSERT *
```

---

## 4. What is Time Travel?

[Time Travel](https://docs.databricks.com/en/delta/history.html) allows users to query previous versions of a Delta table.

Example:

```sql
SELECT *FROM sales VERSION AS OF 5;
```

or

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

### Key Benefits for Your Interviews

- Data recovery
- Auditing
- Debugging
- Reproducible analytics

---

## 5. What is Change Data Feed (CDF)?

[CDF](https://docs.databricks.com/en/delta/delta-change-data-feed.html) records row-level changes in Delta tables.

It tracks:

- Inserts
- Updates
- Deletes

Example:

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

Useful for:

- Incremental ETL
- CDC Pipelines
- Data synchronization

---

## 6. Explain Medallion Architecture

The [Medallion Architecture](https://www.databricks.com/glossary/medallion-architecture) organizes data into layers.

### Bronze Layer

- Raw data
- Minimal transformations

### Silver Layer

- Cleansed
- Validated
- Joined
- Deduplicated

### Gold Layer

- Business-ready
- Aggregated
- Reporting datasets

---

## 7. What is Unity Catalog?

[Unity Catalog](https://docs.databricks.com/en/data-governance/unity-catalog/index.html) is Databricks’ centralized governance solution.

Features include:

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

---

## 8. Hive Metastore vs Unity Catalog

| Hive Metastore | Unity Catalog |
| --- | --- |
| Workspace level | Account level |
| Limited governance | Centralized governance |
| Basic permissions | Fine-grained access |
| No lineage | Built-in lineage |
| Limited sharing | Delta Sharing |

---

## 9. What is Photon?

[Photon](https://docs.databricks.com/en/compute/photon.html) 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](https://docs.databricks.com/en/delta/clustering.html) automatically reorganizes table data without repartitioning.

Advantages:

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

---

## 11. Partitioning vs Liquid Clustering

| Partitioning | Liquid Clustering |
| --- | --- |
| Static | Dynamic |
| Manual | Automatic |
| Can create small files | Reduces fragmentation |
| Hard to modify | Easy to evolve |

---

## 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](https://srinimf.com/2025/12/18/databricks-auto-loader-s3-medallion-architecture/)

[Auto Loader](https://docs.databricks.com/en/ingestion/cloud-object-storage/auto-loader/index.html) 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](https://docs.databricks.com/en/structured-streaming/index.html) processes continuous data streams using the Spark DataFrame API.

Supports:

- Kafka
- Event Hubs
- Kinesis
- Files

Processing Modes

- Micro-batch
- Continuous

---

## 15. Batch vs Streaming Processing

| Batch | Streaming |
| --- | --- |
| Historical data | Real-time data |
| Finite | Infinite |
| Scheduled | Continuous |
| Higher latency | Low latency |

---

## 16. What is Adaptive Query Execution (AQE)?

[AQE](https://docs.databricks.com/en/optimizations/aqe.html) optimizes queries during execution.

Features:

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

---

## 17. Broadcast Join vs Shuffle Join

### Broadcast Join Concepts

- Small table
- No shuffle
- Faster

### Shuffle Join Concepts

- Large tables
- Data movement required
- Higher execution time

---

## 18. What Causes Data Skew?

Common causes include:

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

Mitigation strategies:

- Salting
- AQE
- Repartitioning
- Broadcast joins

---

## 19. What is Z-Ordering?

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

Example:

```
OPTIMIZE salesZORDER BY (customer_id);
```

Benefits:

- Faster filtering
- Reduced IO
- Improved query performance

---

## 20. What is VACUUM?

[VACUUM](https://docs.databricks.com/en/delta/vacuum.html) removes old files no longer referenced by a Delta table.

Example:

```
VACUUM sales RETAIN 168 HOURS;
```

---

## 21. OPTIMIZE vs VACUUM

| OPTIMIZE | VACUUM |
| --- | --- |
| Compacts files | Deletes old files |
| Improves performance | Frees storage |
| Does not delete history | Removes obsolete data files |

---

## 22. What is [Delta Live Tables](https://srinimf.com/2025/10/20/delta-live-tables-dlt-optimization-tips-improve-performance-quality-reliability/) (DLT)?

[DLT](https://docs.databricks.com/en/delta-live-tables/index.html) 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](https://docs.databricks.com/en/workflows/index.html) 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](https://docs.databricks.com/en/delta-sharing/index.html) securely shares live data across organizations without copying data.

Supports:

- Open protocol
- Cross-cloud sharing
- External partners

---

## 28. What are Databricks Asset Bundles?

[Asset Bundles](https://docs.databricks.com/en/dev-tools/bundles/index.html) 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?

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 an 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 on Databricks RSA Interview Questions

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. You can also [contact us](/contact-us/) or visit the [About Srini](/about/) page to learn more about the author and available coaching resources.

Mastering topics such as [Delta Lake](https://docs.databricks.com/en/delta/index.html), [Unity Catalog](https://docs.databricks.com/en/data-governance/unity-catalog/index.html), [Workflows](https://docs.databricks.com/en/workflows/index.html), [Auto Loader](https://docs.databricks.com/en/ingestion/cloud-object-storage/auto-loader/index.html), [Structured Streaming](https://docs.databricks.com/en/structured-streaming/index.html), [Photon](https://docs.databricks.com/en/compute/photon.html), [Liquid Clustering](https://docs.databricks.com/en/delta/clustering.html), and performance tuning will help you confidently answer most RSA interview questions.

Related[Top 15 Delta Lakehouse Interview & Certification Questions](https://srinimf.com/2025/05/24/top-15-delta-lakehouse-interview-certification-questions/)

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