Last updated on April 7th, 2025 at 03:23 am
Here, we will discuss Azure Databricks Interview Questions, which interviewers ask in most company interviews for mainly Data Engineer job positions.
Table of Contents
1. What is Azure Databricks?
Azure Databricks is a cloud-based analytics platform optimized for Azure, built on Apache Spark, enabling big data processing and machine learning.
Apache Spark is an open-source distributed computing engine for large-scale data processing.
It offers in-memory processing, speed, and ease of use via APIs (Python, Scala, Java, R). Core components: Spark Core (RDDs), Spark SQL, MLlib, Streaming, and GraphX.
2. Azure Databricks Interview Topics
- Core Components: Workspace (collaboration), Clusters (compute), Notebooks (code), Jobs (automation).
- Delta Lake: ACID transactions, schema enforcement, time travel for reliable data lakes.
- Integration: Azure Blob Storage, Data Lake, Synapse, DevOps.
- Security: VNet, encryption, RBAC, Azure AD.
- Optimization: Auto-scaling, caching, Spark tuning.
- ML Integration: MLflow tracking, AutoML, collaborative workflows.
- Use Cases: ETL, real-time analytics, ML pipelines.
- Cost Management: Auto-termination, spot instances, cluster policies.
- Data Ingestion: Structured Streaming, Delta Lake, connectors.
- Monitoring: Ganglia, Spark UI, audit logs.
Azure Databricks Interview Questions
1. How do PySpark data frames work?
The distributed collection of structured data is called a PySpark DataFrame. They are stored in named columns and are equivalent to relational database tables.
Additionally, PySpark DataFrames are more effectively optimized than Python or R code. Various sources, including Structured Data Files, Hive Tables, external databases, existing RDDs, etc., can be used to create them.
2. Define PySpark partition. What’s the maximum number of partitions Pyspark allows?
The PySpark Partition method divides a large dataset into smaller datasets using one or more partition keys. When transformations on partitioned data run more quickly, execution performance is improved. This is due to the concurrent operation of each partition’s transformations.
PySpark supports two partitioning methods: partitioning in memory (DataFrame) and partitioning on disc (File system). partitionBy (self, *cols) is the syntax for it. Adding 4x as many partitions are accessible to the cluster application core count is advisable.
3. How do you import data into Delta Lake?
Loading data into Delta Lake is quite simple. Using Databricks Auto Loader or the COPY INTO command with SQL, you can automatically intake new data files into Delta Lake as they arrive in your data lake (for example, on S3 or ADLS).
Additionally, you can batch-read your data using Apache SparkTM, make any necessary changes, and then store the outcome in Delta Lake format.
4. Does Delta Lake offer access controls for security and governance?
You can leverage access control lists (ACLs) to set permissions for workspace objects (folders, notebooks, experiments, and models), clusters, pools, tasks, data schemas, tables, views, etc., using Delta Lake on Databricks. Both administrators and users with delegated access control list management rights have this access.
5. What does an index mean in the context of Delta Lake?
An index is a data structure that supports Delta Lake in improving query performance by enabling fast data lookups. It is possible to speed up queries that filter on a particular column or columns by using an index on a Delta table.
6. Is PySpark DataFrames’ implementation entirely different from other Python DataFrames like Pandas, or are there some similarities?
You should use DataFrames in Spark rather than Pandas. To lessen the performance impact of switching between the two frameworks, users of Pandas and Spark DataFrames should consider implementing Apache Arrow.
7. Is it possible to utilize multiple languages in one notebook, or are there substantial restrictions? Is it usable in later stages if you build a DataFrame in your Python notebook using a % Scala magic?
A Scala DataFrame can be produced and then used as a Python reference. Write your program in Scala or Python if possible.
8. Which programming languages can be used to integrate with Azure Databricks?
Python, Scala, and R are a few examples of languages that you can use with the Apache Spark framework.
Azure Databricks also supports SQL as a database language.
9. Suppose you have just begun your job at XYZ Company. Your manager has instructed you to develop business analytics logic in the Azure notebook, leveraging some of the general functionality code written by other team members. How would you proceed?
You must import the databricks code into your notebook to reuse it. There are two additional options to import the code.
If the code is present in the same workspace, you can import it immediately.
If the code is outside the workspace, you should build a jar or module out of it and import it into the databricks cluster.
10. How do you handle the Databricks code while using TFS or Git in a collaborative environment?
The initial problem is the lack of support for Team Foundation Server (TFS). You can only use Git or a repository system built on the distributed format of Git.
While it would be ideal to link Databricks to your Git directory of notebooks, you can still consider Databricks as a replica of your project, even though this is not presently feasible.
Your first step is to start a notebook, which you then update before submitting to version control.
11. How to convert the block from a Python/Scala notebook to a Simple SQL Notebook.
If you type “%sql” at the beginning of any block in the Databricks notebook, the block will be converted from a Python/Scala notebook to a Simple SQL Notebook.
12. What is the method for creating a Databricks private access token?
Go to the “user profile” icon and choose “User settings” to create a private access token. You must choose the “Access Tokens” tab to access the “Generate New Token” button. To create the token, press the right button.
13. Define a databricks secret?
A secret is a key-value pair that can be used to safeguard confidential data. It consists of a special key name enclosed in a secure environment. There are only 1000 secrets allowed per scope. There is a 128 KB maximum size limit.
14. What possible challenges might you come across using Databricks?
If you don’t have enough credits to construct more clusters, you might experience cluster creation failures. If your code is incompatible with the Databricks runtime, Spark errors will occur.
Network issues may occur if your network is not set up correctly or if you try to access Databricks from an unsupported location.
