Preparing for an Azure Synapse Interview? This curated list of Azure Synapse Interview Questions covers technical expertise and analytical thinking.
Table of Contents
1. What is Azure Synapse Analytics
Azure Synapse Analytics is a unified cloud analytics service by Microsoft that integrates enterprise data warehousing (SQL-based) and big data processing (Spark-based) into a single platform.
It enables seamless data ingestion, transformation, and analysis across structured and unstructured sources, supporting real-time analytics, machine learning, and BI via tools like Synapse Pipelines, Power BI, and Azure ML.
It offers serverless and dedicated SQL pools, Apache Spark integration, and seamless data lake connectivity.
2. Azure Synapse Interview Topics
Scenario-Based: Real-time IoT data pipelines, data archiving, troubleshooting slow queries, Synapse vs. Databricks/Blob Storage
Core Concepts: Dedicated vs. serverless SQL pools, Spark pools, data distribution (hash/round-robin), MPP architecture.
Data Integration: Synapse Pipelines (ETL/ELT), Linked Services, data ingestion from 90+ sources, OPENROWSET function.
Security & Governance: RBAC, dynamic data masking, encryption (at rest/transit), compliance (GDPR).
Performance Optimization: Partitioning, indexing, materialized views, query tuning.
Azure Synapse Interview Questions
1. How does Azure Synapse differ from Databricks?
Azure Synapse is a data integration service with some amazing transformation capabilities, while Azure Databricks is a data analytics-focused platform built on top of Spark.
Azure Synapse integrates big data analytics and an enterprise data warehouse into a single platform. Databricks allows customers to develop complex machine learning algorithms and perform big data analytics.
However, both Synapse Analytics and Azure Databricks can be used together when building a data pipeline.
2. Define a linked service in Azure Synapse Analytics.
The external sources outside the Azure Synapse Analytics workspace are connected via Linked Services.
A linked service is required in a data pipeline to read or write data to a source or destination.
For instance, you can use it to establish a connection to the Azure Data Lake storage account to execute SQL queries on the files.
Suppose you want to pull data from an on-premises server into the cloud or connect to the Azure data lake storage account to perform SQL queries on the files.
In these cases, you must build a linked service by providing data such as a username, password, and server address to establish a connection.
3. What do you understand by SQL pool in Azure Synapse Analytics?
The default SQL pool in Azure Synapse Analytics helps with query optimization, data distribution, and data compression.
4. What does the Azure Synapse Analytics OPENROWSET function do?
The OPENROWSET function lets data engineers read data from diverse data sources, including flat files, RDBMSs, and other OLE DB sources.
In Azure Synapse, one uses the OPENROWSET function to read the file as a table.
For instance, you want to perform the queries on a file saved in the ADLS account.
Using the rowset function, which converts each row of a file into a row of a table, you can read this file as a table.
5. What makes Synapse Analytics different from Azure Blob Storage?
Large volumes of unstructured data, such as video, audio, and images, can be stored in the cloud using Azure Blob storage.
You can gather, store, and analyze huge amounts of data with the help of Azure Synapse Analytics, a cloud-based data warehouse service.
In a nutshell, Azure Blob Storage majorly focuses on storing and retrieving data, while Azure Synapse Analytics focuses on preparing, managing, and analyzing data.
6. Assume you are the lead data engineer at your company, and a few Spark notebooks have been run in the past few hours. You want to examine each notebook execution. Where will you find the past few hours’ histories and retrieve the event log for them in Azure Synapse Analytics?
- You must access the monitor tab in Azure Synapse Analytics.
- You can find the activities area in the monitor tab on the left.
- The Apache Spark application has a separate tab below it.
- The Spark history server will appear on the following page when you click on it.
- Simply click on that to view the whole history and to download the event log for a particular application run.
7. Suppose you are a data engineer for the XYZ company. The company wants to transfer its data from an on-site server to the Azure cloud. How will you achieve this using Azure Synapse Analytics?
You must develop an integration runtime to transfer data from an on-premises server to a cloud server. This integration runtime will act as the self-hosted IR since the on-premise servers cannot be connected using the auto-resolve integration runtime.
After creating the self-hosted IR, you can use the copy function to build a pipeline that will transfer data from the on-premise server to the cloud server.
8. Suppose you are the lead data engineer at XYZ company, which is transitioning from on-premises to the cloud. On one of their on-site servers, your organization holds some mission-critical data. As a result, you must ensure that the team’s self-hosted IR is only accessible to specified team members. What is the best way to meet this requirement using Azure Synapse Analytics?
- Open the Azure Synapse Analytics workspace studio.
- Navigate to the access control section under the monitor tab.
- You can grant access at the workspace item level there.
- Choose the IR item and the IR’s name, role, and member data.
9. Assume that a different application will access the data created by your stored procedure in the SQL pool database. Daily updates of this data are necessary. What approach would you apply to address this issue?
- To begin, open the Azure Synapse Analytics workspace and create the pipeline in the integration tab of the resource.
- The activity for the SQL pool stored procedure must be added to this pipeline.
- Choose the specified stored procedure from the SQL pool stored procedure activity.
- Schedule this pipeline by including the trigger that will run it daily.
10. Let’s say you want to know how many SQL requests your team has already executed in the SQL pool. Where can you see a list of every historical query run in the Azure Synapse Analytics workspace?
- Any time you perform a query in Azure Synapse Analytics, a history log is generated.
- You must select the Manage tab to see the SQL request.
- You can sort by time to see the executions and their respective results.
11. What does the synapse analytics control node do?
The primary component of a Synapse SQL architecture is the control node. A distributed query engine is executed on the Control node to optimize and coordinate parallel queries.
The Control node converts a T-SQL query into parallel queries executed against each distribution when submitted to a specific SQL pool.
The DQP engine, used in serverless SQL pools, runs on the Control node and divides user queries into smaller ones that Compute nodes will process to optimize and coordinate distributed execution.
12. What is the method to create a pipeline in Azure Synapse Analytics?
You can create a pipeline in Azure Synapse with the following steps:
- Open the Integrate hub in Synapse Studio.
- Click on ‘Pipeline’ to build a new pipeline. Open the Pipeline designer by clicking on the newly created pipeline object.
- You can start dragging the activities from the ‘Activities’ panel on the left into the blank space in the middle.
- You must publish your pipeline once it has been created. After that, you can test it to ensure there are no errors and verify that it works.
13. In Azure Synapse Analytics, how much data can you keep in a single column simultaneously?
The maximum size of a single column in Azure Synapse Analytics depends on the storage technology used and also on the data type of the column.
You can store up to 1 billion 2-byte Unicode characters using nvarchar [(n | max)].
14. Briefly discuss setting a Spark job in Azure Synapse Analytics.
- Create a Spark Pool
- Prepare the Data
- Write the Spark Job
- Submit the Spark Job
- Monitor the Spark Job using Synapse Analytics Studio
- Schedule the Job
15. Mention some of the data security features offered by Synapse Analytics.
Synapse Analytics provides certain data security features for dedicated SQL pools, including Data Discovery & Classification, Dynamic Data Masking, Vulnerability Assessment, Advanced Threat Protection, and Transparent Data Encryption.
Synapse Analytics supports encryption of data both in transit and at rest.
Data can be encrypted using Azure Key Vault, which provides a secure way to store and manage encryption keys.