Top Azure Synapse Interview Questions (2024)
What is Azure Synapse used for?
What are the main components of Azure Synapse?
How many nodes does Azure synapse have?
What is dedicated SQL pool or SQL DW in Azure synapse?
What is DWU in Azure synapse?
What is distribution in Azure synapse?
What is the difference between Azure synapse and Azure SQL data warehouse?
What is Synapse architecture?
What is workload management in synapse?
What is the purpose of workload classifier?
What are the security that synapse analytics provide for data?
How do you implement row level security in synapse?
What is difference between Azure Databricks and azure synapse?
What is the maximum amount of data that could be stored in a single column in Azure Synapse Analytics?
How can SQL Server store characters greater than 4000 in NVARCHAR(max)?
Q: What is Azure Synapse used for?
Ans:
Data integration, enterprise data warehousing, and big data analytics are all part of Azure Synapse Analytics, an unlimited analytics service. It allows users to query data at scale on your own terms, utilizing either serverless or dedicated solutions.
Q: What are the main components of Azure Synapse?
Ans:
- Synapse Pipelines
- Synapse Spark
- Synapse SQL
- Azure Data Lake Storage
- Power BI
Q: How many nodes does Azure synapse have?
Ans:
The dedicated SQL pool's service level determines the number of compute nodes, which might range from 1 to 60.
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Q: What is dedicated SQL pool or SQL DW in Azure synapse?
Ans:
A dedicated SQL pool (originally SQL DW) represents a collection of allocated analytic resources. The term "analytic resources" refers to a combination of CPU, memory, and I/O resources.
Q: What is DWU in Azure synapse?
Ans:
Data Warehouse Units are compute scale units that combine CPU, memory, and I/O resources (DWUs). A DWU is a normalised, abstract measure of compute resources and performance.
When users modify the service level, the number of DWUs available to the system changes, which affects the system's performance and cost.
We can increase the number of data warehouse units for better performance. Decrease the number of data warehouse units for better performance. Because computation and storage expenses are billed separately, altering data warehouse units has no impact on storage costs.
Q: What is distribution in Azure synapse?
Ans:
A distribution is the basic unit of storage and processing for parallel queries that operate on distributed data. When Synapse SQL executes a query, it divides it into 60 smaller queries that execute in parallel. One of the data distributions is used by each of the 60 smaller searches.
Q: What is the difference between Azure synapse and Azure SQL data warehouse?
Ans:
The Azure SQL database is suitable for data warehouses with minimal data sets and small data volumes. It has a simple maintenance process, predictable costs, and configurable RPOs. Whereas Azure Synapse with SQL Pool can handle a big amount of data for a more complex data warehouse.
Q: What is Synapse architecture?
Ans:
Synapse's architecture is based on nodes. Synapse is based on a distributed computation architecture that spreads data processing capacity across numerous nodes. The computing and storage nodes are independent of one another, with a range of 1 to 60 compute nodes. A Data Warehouse Unit (DWU) is a unit of compute power.
Q: What is workload management in synapse?
Ans:
In Azure Synapse, dedicated SQL pool workload management is divided into three high-level ideas.
- Workload Classification
- Workload Importance
- Workload Isolation
Q: What is the purpose of workload classifier?
Ans:
The dedicated SQL pool's service level determines the number of compute nodes, which might range from 1 to 60.
Q: How many nodes does Azure synapse have?
Ans:
Classification helps to route requests to a work load based on a set of rules. The classifier allocates incoming requests to a workload group depending on the parameters supplied in the classifier statement specification. Every time a request is sent, the classifiers are reviewed. If no classifier is found for a request, it is assigned to the default workload group.
Q: What are the security that synapse analytics provide for data?
Ans:
Data Discovery & Classification, Dynamic Data Masking, Vulnerability Assessment, Advanced Threat Protection, and Transparent Data Encryption are some of the data protection features offered by Synapse Analytics for dedicated SQL pools.
Q: How do you implement row level security in synapse?
Ans:
Use the CREATE SECURITY POLICY Transact-SQL statement to implement RLS. Predicates
are produced
as inline table-valued functions using a Transact-SQL command.
Two types of security predicates are supported by RLS.
- Filter predicates filter the rows that are available for reading operations invisibly (SELECT, UPDATE, and DELETE).
- Write operations that break the predicate are explicitly blocked by block predicates (AFTER INSERT, AFTER UPDATE, BEFORE UPDATE, BEFORE DELETE).
Q: What is difference between Azure Databricks and azure synapse?
Ans:
Azure Synapse combines enterprise data warehouse and big data analytics into a single platform. Whereas Databricks not only performs big data analytics but moreover enables users to create complicated machine learning (ML) solutions at scale.
Q: What is the maximum amount of data that could be stored in a single column in Azure Synapse
Analytics?
Ans:
By using nvarchar [ ( n | max ) ], we can store up to 1 billion 2-byte Unicode
characters.
Q: How can SQL Server store characters greater than 4000 in NVARCHAR(max)?
Ans:
Internally, SQL Server keeps the maximum columns in custom structures, allowing it to work around the SQL Server page restriction of 8K. It works, but it takes more effort than simply storing a few hundred bytes on a page, therefore this storage technique puts a greater burden on SQL Server.
If our dynamic string is longer than 8000 characters, it should be split into three variables.
-- each variable can keep max 4000 character
Declare @MAXSQL1 as nvarchar(Max)
,@MAXSQL2 as nvarchar(Max)
Set @MAXSQL1 = 'Select * '
Set @MAXSQL2 = 'From table A'
EXEC (@MAXSQL1+@MAXSQL2)