Azure synapse Interview Questions and Answers

1. What is the difference between a Dedicated SQL Pool and a Serverless SQL Pool?

Dedicated SQL Pool is a provisioned resource with dedicated compute and storage whereas serverless SQL Pool is a pay-per-query model where compute resources are provisioned on demand. Dedicated SQL Pool is used for running high-performance analytics queries and suitable for large-scale data warehousing. Serverless SQL Pool is ideal for ad hoc analysis and querying data in the data lake without upfront infrastructure costs.

2. What are the key components of Azure Synapse Analytics?

Key components of Azure Synapse Analytics

SQL Pools for running SQL-based queries. Apache Spark Pools is used for running big data analytics using Spark. Data integration by Built-in pipelines for ETL/ELT processes. Synapse Studio is a unified workspace for data preparation, management, and analytics. Azure Data Lake Storage Gen2 is for scalable data storage and management.

3. How does Azure Synapse integrate with Azure Data Lake Storage Gen2?

Azure Synapse integrates with Azure Data Lake Storage Gen2 that allows seamless querying, analysis, and transformation of data stored in the lake. Serverless SQL Pools and Spark Pools can read and write data directly from the data lake. It enables a unified analytics experience.

4. Explain the concept of a Workspace in Azure Synapse Analytics.

In Azure Synapse Analytics, the workshop is a central hub where users can manage SQL pools, Spark pools, data pipelines, linked services, and other resources. It provides a collaborative environment for data engineers, data scientists, and analysts to work on end-to-end analytics tasks.

5. What are Synapse Pipelines, and how do they compare to Azure Data Factory?

Synapse Pipelines are data integration capabilities of Azure Synapse which are similar to Azure Data Factory (ADF). They allow for creating ETL/ELT workflows to move and transform data. The primary difference is that Synapse Pipelines are integrated into Synapse Studio, providing a more unified analytics experience.

6. What is PolyBase in Azure Synapse Analytics?

In Azure Synapse Analytics, PolyBase is a feature that allows querying external data sources like:

Azure Blob Storage,
Azure Data Lake,
SQL Server.

It enables querying external data using T-SQL without moving the data into the Synapse SQL Pool.

7. What is a Dedicated SQL Pool ?

Dedicated SQL Pool in Azure Synapse Analytics is a provisioned compute resource designed for large-scale, high-performance analytics. It supports distributed computing for querying large volumes of data and can scale based on demand.

8. What is a Synapse Link and its uses?

Synapse Link is an important feature that enables near-real-time analytics by connecting operational data sources like Azure Cosmos DB and Azure SQL Database to Azure Synapse Analytics. It allows analytics on live operational data without ETL pipelines.

9. How do you monitor and optimize query performance in Azure Synapse Analytics?

Monitoring and optimization of query performance can be done through:

SQL Query Performance Insights: To analyze query plans and execution times.

Dynamic Management Views (DMVs): Retrieving performance metrics and diagnostics.

Workload Management: Assigning resource classes and workload groups to manage query priorities.

Query Store: Tracking query performance over time.

10. What is a Spark Pool, and when would you use it in Azure Synapse?

Spark Pool is a managed Spark cluster for big data processing and machine learning tasks. It can be used to process large volumes of data using Spark’s parallel processing capabilities, or for data transformation, ETL jobs, and advanced analytics.

11. What are the key security features in Azure Synapse Analytics?

Key security features in Azure Synapse Analytics

Data Encryption: To perform encryption at rest and in transit.

Azure Active Directory (AAD): Authentication and role-based access control (RBAC).

Column-level Security: Restrict access to specific columns.

Dynamic Data Masking: Mask sensitive data in query results.

Firewall Rules and Private Endpoints: Secure network access.

12. How does Azure Synapse support real-time analytics?

Azure Synapse supports real-time analytics through:

Synapse Link for near-real-time analytics on operational data.
Streaming Data Ingestion using Event Hub or Azure Data Explorer.
Serverless SQL Pools for ad hoc analysis on continuously updated data in the lake

13. What is the difference between Azure Synapse Analytics and Azure Databricks?

Azure Synapse Analytics is best for data warehousing, SQL-based analytics, and integrated data lake querying. Azure Databricks is Ideal for advanced analytics, machine learning, and big data processing using Apache Spark.

14. What is the role of Synapse Studio?

Synapse Studio is the web-based integrated workspace where users can develop, manage, and analyze data. It supports

SQL querying,
Spark development,
Data integration pipelines,
Visualization within a single interface.

15. How does partitioning work in a Dedicated SQL Pool?

Partitioning helps improve query performance by dividing large tables into smaller, manageable segments based on a specific column. This allows the engine to scan only relevant partitions and commonly used for large fact tables in data warehousing.

16. What is the role of Dataflows in Azure Synapse Analytics?

Dataflows are used for creating data transformation logic visually without writing code. They help in building ETL/ELT processes with a no-code interface. It makes it easier for data engineers to cleanse and shape data.

17. What are Materialized Views in Azure Synapse Analytics?

Materialized Views are precomputed, persisted query results that improve performance for complex and frequently run queries. It stores data and can automatically refresh when the underlying data changes, providing faster query results.

18. Explain Distribution Types in Azure Synapse Dedicated SQL Pool.

Azure Synapse Dedicated SQL Pool uses three distribution types to optimize data storage and querying:

Hash distributed,
Round robin distributed,
Replicated

Hash Distributed distributes data across nodes based on a hash key which is ideal for large distributed tables. Round-Robin Distributed distributes data evenly across all nodes without any key and is suitable for staging tables. Replicated copies data to all nodes and used for small tables that are joined frequently with larger tables.

19. What are the common use cases for Azure Synapse Analytics?

Use cases of Azure Synapse Analytics

Enterprise data warehousing,
Big data analytics,
Real-time analytics,
ETL/ELT pipelines,
Machine learning and AI.

20. What is the difference between a Pipeline and a Data Flow in Synapse Analytics?

Pipeline orchestrates the movement of data from source to destination and manages ETL/ELT workflows. Data Flow is a visual, no-code way to design complex data transformations within a pipeline.

21. What is the difference between Pipeline Triggers and Manual Execution in Synapse Pipelines?

Pipeline triggers automatically start pipelines based on events, schedules, or tumbling windows. Manual execution refers to starting a pipeline manually by a user from Synapse Studio.

22. How do you handle data loading in a Dedicated SQL Pool for large datasets?

To optimize data loading for large datasets:

Use PolyBase for bulk data loading
COPY Statement is suitable for efficient, parallel loading of data.
Batch loading loads data in smaller batches to avoid performance bottlenecks.
Use CTAS (CREATE TABLE AS SELECT) to pre-aggregate or transform data during the load process.

23. What is the purpose of Resource Classes in Azure Synapse Analytics?

Resource Classes control the amount of compute and memory resources allocated to a query in a Dedicated SQL Pool. Larger resource classes improve query performance but reduce concurrency. Resource classes are assigned based on user roles.

24. What are Materialized Views in Azure Synapse Analytics, and why are they useful?

Materialized Views are precomputed result sets stored physically in the database. They improve query performance by avoiding expensive computations on frequently queried data. Materialized Views are automatically refreshed when underlying data changes which makes them ideal for optimizing analytics workloads.

25. What are the different types of indexes supported in Synapse Dedicated SQL Pools?

Types of indexes supported in Synapse Dedicated SQL Pools

Clustered Columnstore Index is the default index for tables, optimized for analytics workloads and compresses data.

Clustered Index is row-based index for faster querying on smaller datasets or transactional workloads. Non-Clustered Index supports additional query patterns by indexing specific columns.

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Azure synapse Interview Questions and Answers