Azure Databricks Certification DP-750 Interview Questions Answer

INTERMEDIATE LEVEL

1. What is Azure Databricks?

Answer:
Azure Databricks is a cloud-based data and AI platform built on Apache Spark and integrated with Microsoft Azure. It provides capabilities for data engineering, analytics, machine learning, and data governance. Data engineers can use it to build scalable pipelines, process large datasets, work with Delta Lake, and manage enterprise data workloads.

2. What is the role of Apache Spark in Azure Databricks?

Answer:
Apache Spark provides the distributed processing engine behind many Azure Databricks workloads. It enables large datasets to be processed across multiple compute nodes. Spark supports languages such as Python, SQL, Scala, and R and provides APIs for batch processing, streaming, data transformation, and analytics.

3. What is Delta Lake?

Answer:
Delta Lake is a storage layer that adds reliability and transactional capabilities to data lakes. It provides features such as ACID transactions, schema enforcement, schema evolution, time travel, and scalable metadata management. In Azure Databricks, Delta Lake is commonly used for creating reliable data pipelines and lakehouse architectures.

4. What is a Databricks notebook?

Answer:
A notebook is an interactive development environment where users can write and execute code. Azure Databricks notebooks support Python, SQL, Scala, and R. They are commonly used for data exploration, transformation, testing, pipeline development, and documenting data engineering workflows.

5. What is Unity Catalog?

Answer:
Unity Catalog is Azure Databricks' centralized governance solution for managing data and AI assets. It provides centralized access control, data discovery, auditing, lineage, and governance across supported workspaces and data assets.

6. What is a Databricks cluster?

Answer:
A Databricks cluster is a collection of compute resources used to execute data processing workloads. It typically consists of a driver node and worker nodes. The driver coordinates execution, while workers perform distributed processing tasks.

7. What is the difference between a driver and worker node?

Answer:
The driver node manages the Spark application, maintains execution information, and coordinates tasks. Worker nodes execute the actual processing tasks. The driver distributes work to workers and collects the required results.

8. What is schema enforcement in Delta Lake?

Answer:
Schema enforcement ensures that incoming data conforms to the existing table schema. If incompatible data is written, Delta Lake can reject the operation rather than silently corrupting the table. This improves data quality and reliability.

9. What is schema evolution?

Answer:
Schema evolution allows the structure of a Delta table to change as new data arrives. For example, a new column can be added to an existing table when configured appropriately. It is useful when source systems evolve over time.

10. What is Delta Lake time travel?

Answer:
Time travel allows users to query previous versions of a Delta table. It can be used for auditing, troubleshooting, historical analysis, and recovering from accidental changes. Users can reference a previous table version or timestamp when supported.

11. What is a medallion architecture?

Answer:
Medallion architecture organizes data into progressive layers, commonly called Bronze, Silver, and Gold.

• Bronze: Raw ingested data
• Silver: Cleaned and transformed data
• Gold: Business-ready and aggregated data

This approach improves data quality, maintainability, and pipeline organization.

12. What is Auto Loader?

Answer:
Auto Loader is an Azure Databricks capability for incrementally and efficiently ingesting new files from cloud storage. Instead of repeatedly scanning an entire directory, it tracks new files and processes them incrementally, making it suitable for scalable ingestion pipelines.

13. What is Structured Streaming?

Answer:
Structured Streaming is Spark's stream-processing framework. It allows developers to process continuously arriving data using DataFrame and SQL APIs. It can be used for real-time or near-real-time pipelines involving sources such as event streams and cloud storage.

14. Why are partitions important in Spark?

Answer:
Partitions divide data into smaller logical pieces that can be processed in parallel. Proper partitioning can improve parallelism and performance. However, excessive or poorly designed partitions can create overhead and lead to inefficient processing.

15. What is the difference between a DataFrame and an RDD?

Answer:
An RDD is Spark's lower-level distributed data abstraction, while a DataFrame provides structured data with named columns and benefits from Spark's optimization engine. DataFrames are generally preferred for modern data engineering because they provide better optimization and easier SQL integration.

ADVANCED LEVEL

1. How would you optimize a slow Spark job in Azure Databricks?

Answer:
I would first inspect the Spark UI and execution plan to identify bottlenecks. Then I would examine data partitioning, joins, shuffles, file sizes, and data-skew issues. Depending on the workload, optimization could include predicate pushdown, selecting only required columns, improving partitioning, broadcasting small tables, optimizing Delta tables, and selecting appropriate compute resources.

2. What is data skew and how can you handle it?

Answer:
Data skew occurs when some partitions contain significantly more data than others. This can cause certain tasks to run much longer than the rest. Techniques such as salting keys, broadcasting small datasets, repartitioning, changing join strategies, and addressing highly skewed keys can help reduce its impact.

3. What is a broadcast join?

Answer:
A broadcast join copies a relatively small dataset to worker nodes so that it can be joined locally with a larger dataset. This can significantly reduce shuffle operations. However, broadcasting an excessively large dataset can cause memory pressure and should therefore be used carefully.

4. What is the difference between repartition and coalesce?

Answer:
repartition() generally performs a shuffle and can increase or decrease the number of partitions. coalesce() is primarily used to reduce partitions and usually avoids a full shuffle. Therefore, coalesce can be more efficient when reducing partitions after processing.

5. How does Delta Lake improve data reliability?

Answer:
Delta Lake provides ACID transactions, schema enforcement, schema evolution, version history, and reliable concurrent operations. These capabilities make data lake storage behave more like a dependable database while retaining the scalability and flexibility of cloud object storage.

6. How would you design an incremental data pipeline using Azure Databricks?

Answer:
I would first identify a reliable incremental mechanism, such as a timestamp, change-tracking column, transaction ID, or Auto Loader. New data would be ingested into the Bronze layer, validated and transformed into Silver, and then aggregated into Gold. Checkpointing, idempotency, monitoring, error handling, and data-quality checks would be included to make the pipeline reliable.

7. What is checkpointing in Structured Streaming?

Answer:
Checkpointing stores information about the progress and state of a streaming query. It allows the streaming application to recover from failures and continue processing from an appropriate point. Checkpoint locations should be persistent and reliably accessible.

8. How would you implement data governance in Azure Databricks?

Answer:
I would use Unity Catalog to centrally manage catalogs, schemas, tables, views, permissions, and data access. I would apply least-privilege access, organize data assets logically, implement auditing and lineage, and use appropriate authentication and authorization controls.

9. What is the purpose of OPTIMIZE in Delta Lake?

Answer:
OPTIMIZE helps improve Delta table performance by compacting smaller files into larger files. This reduces the number of files that Spark needs to read during queries and can improve query performance, especially for workloads that generate many small files.

10. What is Z-Ordering?

Answer:
Z-Ordering is a data-layout optimization technique that colocates related data based on selected columns. It can improve data skipping for queries that frequently filter on those columns. It should be applied selectively based on actual query patterns rather than indiscriminately.

11. What is the small-file problem?

Answer:
The small-file problem occurs when a data lake contains a very large number of small files. Reading many small files creates metadata and I/O overhead, which can slow down queries and pipelines. Delta optimization and appropriate write strategies can help mitigate the issue.

12. How would you handle schema changes in a production pipeline?

Answer:
I would establish a controlled schema-evolution strategy. Expected additive changes, such as new columns, can be handled through supported schema evolution mechanisms. Breaking changes should be validated before deployment. Data-quality checks, version control, testing, monitoring, and rollback procedures should also be incorporated.

13. How can you make a Databricks pipeline idempotent?

Answer:
An idempotent pipeline produces the same correct result when the same input is processed multiple times. This can be achieved using unique business keys, deterministic transformations, merge/upsert operations, transaction tracking, checkpointing, and carefully designed incremental processing logic.

14. How would you troubleshoot a failed production Databricks pipeline?

Answer:
I would start by checking the job run details, error messages, task failures, cluster events, and Spark UI. Then I would identify whether the problem is related to data quality, code, schema changes, connectivity, permissions, resource constraints, or infrastructure. After resolving the root cause, I would rerun the appropriate failed portion while ensuring that duplicate processing does not occur.

15. How would you design a scalable Azure Databricks lakehouse architecture?

Answer:
I would use cloud storage as the scalable data layer, Delta Lake for reliable table storage, and a Bronze-Silver-Gold architecture for progressive data refinement. Unity Catalog would provide centralized governance. Automated ingestion, incremental processing, optimized compute, monitoring, data-quality checks, security controls, and CI/CD practices would be incorporated to support scalable enterprise workloads.