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100 Azure Synapse Analytics Questions

100 Azure Synapse Analytics Questions

Here, I’ve have put together a list of common questions about Azure Synapse Analytics - beginner to advanced levels. These questions are based on real-life situations and cover all the key features of Synapse. Each question comes with multiple-choice answers, and the correct answer is hidden. You can reveal the answer by highlighting the text.

1. What are the steps to create a new dedicated SQL pool in Azure Synapse Analytics?

A) Navigate to the Synapse workspace -> Click on ‘SQL pools’ -> Click ‘New’ -> Provide a name and select performance level -> Click ‘Review + create’.
B) Navigate to Azure Portal -> Click on ‘Resource Groups’ -> Create new Resource Group -> Add SQL Pool.
C) Open Synapse Studio -> Go to ‘Workspace’ -> Select ‘Create SQL Pool’ -> Configure settings.
D) Use Azure CLI to run ‘az sql pool create’.

Answer: A) Navigate to the Synapse workspace -> Click on ‘SQL pools’ -> Click ‘New’ -> Provide a name and select performance level -> Click ‘Review + create’.

2. Which built-in function can you use to load data from Azure Blob Storage into Azure Synapse?

A) COPY
B) BULK INSERT
C) PolyBase
D) Data Factory

Answer: C) PolyBase

3. What is the basic SQL command to create a table in Synapse Analytics?

A) CREATE NEW TABLE table_name (column1 datatype, column2 datatype, …);
B) CREATE TABLE table_name (column1 datatype, column2 datatype, …);
C) NEW TABLE table_name (column1 datatype, column2 datatype, …);
D) TABLE CREATE table_name (column1 datatype, column2 datatype, …);

Answer: B) CREATE TABLE table_name (column1 datatype, column2 datatype, …);

4. How can you pause a dedicated SQL pool to save costs?

A) In the Synapse workspace, go to the SQL pool you want to pause -> Click ‘Pause’.
B) Navigate to Azure Portal -> Click on ‘Resource Groups’ -> Select SQL Pool -> Click ‘Pause’.
C) Open Synapse Studio -> Go to ‘Manage’ -> Select ‘Pause SQL Pool’.
D) Use Azure CLI to run ‘az sql pool pause’.

Answer: A) In the Synapse workspace, go to the SQL pool you want to pause -> Click ‘Pause’.

5. Which tool can you use for monitoring and troubleshooting performance in Synapse Analytics?

A) Azure Monitor
B) Synapse Studio
C) Azure Advisor
D) Log Analytics

Answer: B) Synapse Studio

6. What feature should you enable to secure data in transit within Synapse Analytics?

A) Advanced Threat Protection
B) Firewall Rules
C) Transparent Data Encryption (TDE)
D) Always Encrypted

Answer: C) Transparent Data Encryption (TDE)

7. What is the SQL command to create a view in Synapse Analytics?

A) CREATE VIEW view_name AS SELECT column1, column2, … FROM table_name;
B) CREATE NEW VIEW view_name AS SELECT column1, column2, … FROM table_name;
C) VIEW CREATE view_name AS SELECT column1, column2, … FROM table_name;
D) CREATE TABLE view_name AS SELECT column1, column2, … FROM table_name;

Answer: A) CREATE VIEW view_name AS SELECT column1, column2, … FROM table_name;

8. What is the SQL command to delete a table from your Synapse Analytics database?

A) DELETE TABLE table_name;
B) DROP TABLE table_name;
C) REMOVE TABLE table_name;
D) DESTROY TABLE table_name;

Answer: B) DROP TABLE table_name;

9. What is a best practice for efficiently loading large datasets into Synapse Analytics?

A) Use BULK INSERT
B) Use Data Factory
C) Use PolyBase or COPY statement
D) Use SQL INSERT INTO

Answer: C) Use PolyBase or COPY statement

10. What is the basic SQL command for joining two tables in Synapse Analytics?

A) SELECT * FROM table1 INNER JOIN table2 ON table1.column = table2.column;
B) SELECT * FROM table1 JOIN table2 ON table1.column = table2.column;
C) SELECT * FROM table1, table2 WHERE table1.column = table2.column;
D) SELECT * FROM table1 LEFT JOIN table2 ON table1.column = table2.column;

Answer: B) SELECT * FROM table1 JOIN table2 ON table1.column = table2.column;

11. How can Azure Synapse help a retail company segment their customers based on purchase history?

A) Using Synapse SQL pools to run clustering algorithms on customer data
B) By creating a new SQL database
C) By using Data Factory to transform the data
D) By using Azure Monitor to track customer activities

Answer: A) Using Synapse SQL pools to run clustering algorithms on customer data

12. Which features of Synapse Analytics can be utilized by a financial institution to detect fraudulent transactions in real-time?

A) Synapse Pipelines to integrate with Azure Stream Analytics and Machine Learning models
B) Using Azure Monitor
C) Implementing SQL triggers
D) Using Azure Functions

Answer: A) Synapse Pipelines to integrate with Azure Stream Analytics and Machine Learning models

13. What is the SQL syntax to partition a table to improve query performance in Synapse Analytics?

A) CREATE TABLE table_name (…) WITH (DISTRIBUTION = HASH(column_name));
B) CREATE PARTITIONED TABLE table_name (…) BY (column_name);
C) PARTITION TABLE table_name (…) USING (HASH(column_name));
D) CREATE TABLE table_name (…) PARTITIONED BY (column_name);

Answer: A) CREATE TABLE table_name (…) WITH (DISTRIBUTION = HASH(column_name));

14. How do you implement row-level security in Synapse Analytics?

A) By using security policies and predicates to filter data at the row level
B) By creating separate tables for each user
C) By using Azure RBAC
D) By enabling Transparent Data Encryption (TDE)

Answer: A) By using security policies and predicates to filter data at the row level

15. Which service can you use to automatically scale resources to handle a spike in data ingestion in Synapse Analytics?

A) Synapse SQL pool’s autoscale feature
B) Azure Load Balancer
C) Azure Auto Scale
D) SQL Server Management Studio

Answer: A) Synapse SQL pool’s autoscale feature

16. What techniques can you use to optimize a frequently run query in Synapse Analytics?

A) Indexing, distribution strategies, and partitioning
B) Using temporary tables
C) Creating a new database
D) Increasing the database size

Answer: A) Indexing, distribution strategies, and partitioning

17. What is the SQL command to grant access to a specific user to only one database in Synapse Analytics?

A) GRANT CONNECT TO user_name;
B) GRANT ALL TO user_name;
C) GIVE ACCESS TO user_name;
D) PROVIDE CONNECT TO user_name;

Answer: A) GRANT CONNECT TO user_name;

18. How can you achieve data masking to protect sensitive data in Synapse Analytics?

A) Using Dynamic Data Masking (DDM)
B) Using Transparent Data Encryption (TDE)
C) By encrypting the data at rest
D) By using SQL triggers

Answer: A) Using Dynamic Data Masking (DDM)

19. What is the SQL syntax for creating an external table to query data in Azure Data Lake?

A) CREATE EXTERNAL TABLE table_name (…) WITH (LOCATION = ‘…’, DATA_SOURCE = data_source_name, FILE_FORMAT = file_format_name);
B) CREATE TABLE table_name EXTERNAL (…) WITH (LOCATION = ‘…’, DATA_SOURCE = data_source_name);
C) EXTERNAL CREATE TABLE table_name (…) USING (LOCATION = ‘…’, DATA_SOURCE = data_source_name);
D) CREATE EXTERNAL TABLE table_name (…) USING (LOCATION = ‘…’, FILE_FORMAT = file_format_name);

Answer: A) CREATE EXTERNAL TABLE table_name (…) WITH (LOCATION = ‘…’, DATA_SOURCE = data_source_name, FILE_FORMAT = file_format_name);

20. What tool can you use for scheduling and managing regular data loads from an external source into Synapse Analytics?

A) Azure Data Factory
B) Azure DevOps
C) Azure Monitor
D) Azure Functions

Answer: A) Azure Data Factory

21. Which service integrates with

Synapse Analytics to implement a machine learning model?

A) Azure Machine Learning service
B) Azure Cognitive Services
C) Azure Functions
D) Azure Bot Service

Answer: A) Azure Machine Learning service

22. Which tools can be used for setting up CI/CD pipelines for Synapse Analytics?

A) Azure DevOps or GitHub Actions
B) Azure Pipelines
C) Azure Logic Apps
D) Power Automate

Answer: A) Azure DevOps or GitHub Actions

23. What feature can you use to encrypt data at rest in Synapse Analytics?

A) Transparent Data Encryption (TDE)
B) Always Encrypted
C) SSL/TLS
D) Row-Level Security

Answer: A) Transparent Data Encryption (TDE)

24. Which language and environment would you use to execute a complex data transformation within Synapse?

A) SQL or Spark within Synapse Studio
B) Python within Azure Notebooks
C) R within Azure ML Studio
D) Java within Eclipse

Answer: A) SQL or Spark within Synapse Studio

25. How can you integrate Synapse Analytics with Power BI?

A) Connect Power BI to Synapse Analytics via the dedicated SQL pool connector
B) Export data from Synapse Analytics to CSV and import into Power BI
C) Use Azure Functions to transfer data to Power BI
D) Use Data Factory to connect Power BI to Synapse Analytics

Answer: A) Connect Power BI to Synapse Analytics via the dedicated SQL pool connector

26. What feature can you use to track data changes in your data warehouse within Synapse Analytics?

A) Change Data Capture (CDC)
B) SQL Triggers
C) Data Audit
D) Data Logs

Answer: A) Change Data Capture (CDC)

27. Which component is best suited for real-time data processing in Synapse Analytics?

A) Azure Synapse Data Explorer
B) Azure Functions
C) Azure Logic Apps
D) Azure Databricks

Answer: A) Azure Synapse Data Explorer

28. What tool can assist with data lineage tracking in Synapse Analytics?

A) Azure Purview
B) Azure Monitor
C) Azure Log Analytics
D) Azure Sentinel

Answer: A) Azure Purview

29. Which Synapse feature allows for large-scale data analytics across various data sources?

A) Synapse Pipelines
B) Synapse SQL
C) Azure Data Factory
D) Azure Databricks

Answer: A) Synapse Pipelines

30. How can you execute R and Python scripts in Synapse Analytics?

A) Use Synapse Notebooks
B) Use Azure Functions
C) Use Azure Data Factory
D) Use Azure ML Studio

Answer: A) Use Synapse Notebooks

31. How can a healthcare company ensure data compliance and privacy for sensitive patient data stored in Synapse Analytics?

A) Implementing Dynamic Data Masking and Row-Level Security
B) Using Azure Monitor
C) Implementing SQL triggers
D) Using Azure Load Balancer

Answer: A) Implementing Dynamic Data Masking and Row-Level Security

32. What feature in Synapse Analytics can help optimize query performance by distributing data across different nodes?

A) Data Distribution
B) Sharding
C) Partitioning
D) Replication

Answer: A) Data Distribution

33. How can a financial services company perform complex time-series analysis on transaction data in Synapse Analytics?

A) Using Spark Pools and time-series libraries
B) Using Azure Logic Apps
C) By running SQL scripts
D) By exporting data to a third-party tool

Answer: A) Using Spark Pools and time-series libraries

34. Which method allows you to automate data workflows and orchestrate data movement in and out of Synapse Analytics?

A) Synapse Pipelines
B) Azure Logic Apps
C) Power Automate
D) Azure Functions

Answer: A) Synapse Pipelines

35. How can a manufacturing company analyze IoT sensor data in real-time using Synapse Analytics?

A) Integrating Synapse with Azure Stream Analytics
B) Using SQL triggers
C) Storing data in Blob Storage
D) Using Azure Functions

Answer: A) Integrating Synapse with Azure Stream Analytics

36. How do you manage user permissions and access control in Synapse Analytics?

A) Using Role-Based Access Control (RBAC)
B) Creating separate databases
C) Using SQL triggers
D) By encrypting the data

Answer: A) Using Role-Based Access Control (RBAC)

37. How can a retail company use Synapse Analytics to forecast sales trends?

A) By integrating with Azure Machine Learning for predictive analytics
B) By exporting data to Excel
C) Using SQL scripts
D) By creating new databases

Answer: A) By integrating with Azure Machine Learning for predictive analytics

38. What is the best way to ensure data quality before loading it into Synapse Analytics?

A) Using Data Flows in Synapse Pipelines to perform data cleansing and transformation
B) Using SQL scripts
C) By encrypting the data
D) By storing data in Blob Storage

Answer: A) Using Data Flows in Synapse Pipelines to perform data cleansing and transformation

39. How can you integrate data from various sources such as SQL databases, Blob Storage, and on-premises data into Synapse Analytics?

A) Using Azure Data Factory with Synapse Pipelines
B) Using SQL scripts
C) Using Azure Functions
D) Using Power Automate

Answer: A) Using Azure Data Factory with Synapse Pipelines

40. Which feature allows a retail company to visualize and explore large datasets interactively within Synapse Analytics?

A) Synapse Studio
B) Azure Logic Apps
C) Azure Monitor
D) Azure DevOps

Answer: A) Synapse Studio

41. How can you ensure high availability and disaster recovery for your Synapse Analytics environment?

A) Implementing geo-redundant storage and failover groups
B) Using SQL triggers
C) Using Azure Monitor
D) By encrypting the data

Answer: A) Implementing geo-redundant storage and failover groups

42. What is the purpose of the Synapse SQL Serverless pool?

A) To query data in data lakes without needing to provision dedicated resources
B) To run continuous SQL scripts
C) To monitor database performance
D) To provide disaster recovery solutions

Answer: A) To query data in data lakes without needing to provision dedicated resources

43. Which feature allows you to create and manage data integration pipelines within Synapse Analytics?

A) Synapse Pipelines
B) Azure Logic Apps
C) Power Automate
D) Azure Functions

Answer: A) Synapse Pipelines

44. How can you optimize the performance of a data warehouse query in Synapse Analytics?

A) By creating clustered columnstore indexes
B) By increasing database size
C) By running queries during off-peak hours
D) By exporting data to a third-party tool

Answer: A) By creating clustered columnstore indexes

45. What is the best practice for handling slowly changing dimensions in Synapse Analytics?

A) Using a combination of SQL and Synapse Pipelines to track changes
B) By creating new databases
C) By using Azure Logic Apps
D) By encrypting the data

Answer: A) Using a combination of SQL and Synapse Pipelines to track changes

46. How can you implement real-time analytics on streaming data in Synapse Analytics?

A) By integrating Synapse with Azure Stream Analytics
B) Using SQL triggers
C) By storing data in Blob Storage
D) Using Azure Functions

Answer: A) By integrating Synapse with Azure Stream Analytics

47. Which feature of Synapse Analytics can help you manage and control costs for your data warehouse?

A) Autoscaling SQL pools
B) Using SQL triggers
C) By creating separate databases
D) By encrypting the data

Answer: A) Autoscaling SQL pools

48. What is the benefit of using dedicated SQL pools in Synapse Analytics?

A) They provide optimized performance for large-scale analytics workloads
B) They are always on and consume fewer resources
C) They offer more security features
D) They are easier to configure

Answer: A) They provide optimized performance for large-scale analytics workloads

49. How can you automate the deployment of Synapse Analytics resources using infrastructure as code?

A) Using Azure Resource Manager (ARM) templates
B) By using Azure Monitor
C) Using SQL triggers
D) By encrypting the data

Answer: A) Using Azure Resource Manager (ARM) templates

50. How can a global manufacturing company use Synapse Analytics to unify data from multiple regions for centralized analysis?

A) By setting up a Synapse workspace with integrated data pipelines from each region
B) By creating separate databases for each region
C) Using Azure Logic Apps
D) By storing data in Blob Storage

Answer: A) By setting up a Synapse workspace with integrated data pipelines from each region

51. Which Azure service can be used alongside Synapse Analytics to provide data cataloging and governance capabilities?

A) Azure Purview
B) Azure Monitor
C) Azure DevOps
D) Azure Functions

Answer: A) Azure Purview

52. How can you leverage Synapse Analytics to perform batch processing of large datasets?

A) Using Synapse Pipelines with integrated Spark pools
B) Using SQL triggers
C) By creating new databases
D) Using Power Automate

Answer: A) Using Synapse Pipelines with integrated Spark pools

53. What is a common use case for using Synapse Studio in a data analytics workflow?

A) Interactive data exploration and visualization
B) SQL trigger management
C) Database encryption configuration
D) Network configuration

Answer: A) Interactive data exploration and visualization

54. How can a company ensure their Synapse Analytics data warehouse is secure and compliant with industry standards?

A) Implementing security best practices such as data encryption, access control, and monitoring
B) Using SQL triggers
C) By creating separate databases
D) By using Azure Logic Apps

Answer: A) Implementing security best practices such as data encryption, access control, and monitoring

55. What feature allows Synapse Analytics to handle complex ETL processes and data transformations?

A) Synapse Pipelines with Data Flows
B) SQL scripts
C) Azure Functions
D) Azure DevOps

Answer: A) Synapse Pipelines with Data Flows

56. How can you monitor and troubleshoot Synapse Analytics performance issues?

A) Using built-in monitoring tools in Synapse Studio and Azure Monitor
B) Using SQL triggers
C) By creating new databases
D) By encrypting the data

Answer: A) Using built-in monitoring tools in Synapse Studio and Azure Monitor

57. Which feature in Synapse Analytics helps you to seamlessly integrate data from on-premises and cloud sources?

A) Data integration using Azure Data Factory with Synapse Pipelines
B) SQL scripts
C) Azure Logic Apps
D) Power Automate

Answer: A) Data integration using Azure Data Factory with Synapse Pipelines

58. What type of workloads are best suited for using dedicated SQL pools in Synapse Analytics?

A) Large-scale analytical workloads
B) Small transactional workloads
C) Real-time streaming workloads
D) Configuration management workloads

Answer: A) Large-scale analytical workloads

59. How can a company use Synapse Analytics to support data science and machine learning initiatives?

A) Integrating with Azure Machine Learning and leveraging Spark pools for data processing
B) Using SQL triggers
C) By creating new databases
D) Using Azure Functions

Answer: A) Integrating with Azure Machine Learning and leveraging Spark pools for data processing

60. Which tool can be used to create and manage complex data transformation workflows in Synapse Analytics?

A) Synapse Pipelines
B) Azure Monitor
C) SQL Server Management Studio
D) Azure DevOps

Answer: A) Synapse Pipelines

61. What method can be used to optimize storage costs in Synapse Analytics?

A) Using compression techniques and tiered storage options
B) By increasing database size
C) Using SQL triggers
D) By creating separate databases

Answer: A) Using compression techniques and tiered storage options

62. How can Synapse Analytics support a hybrid data architecture?

A) By integrating with on-premises and cloud data sources using Synapse Pipelines
B) By running SQL scripts only in the cloud
C) By using Azure Logic Apps
D) By creating separate databases

Answer: A) By integrating with on-premises and cloud data sources using Synapse Pipelines

63. What feature in Synapse Analytics can be used to run interactive queries on large datasets without pre-provisioned resources?

A) Synapse SQL Serverless pool
B) Dedicated SQL pool
C) Azure Functions
D) SQL triggers

Answer: A) Synapse SQL Serverless pool

64. Which tool can be used to automate the deployment and management of Synapse Analytics resources?

A) Azure DevOps with CI/CD pipelines
B) Azure Monitor
C) SQL Server Management Studio
D) Power Automate

Answer: A) Azure DevOps with CI/CD pipelines

65. How can a financial company use Synapse Analytics to perform regulatory reporting?

A) By using Synapse Pipelines to aggregate data and generate reports
B) By exporting data to a third-party tool
C) Using SQL triggers
D) By creating separate databases

Answer: A) By using Synapse Pipelines to aggregate data and generate reports

66. What is the benefit of using Spark pools in Synapse Analytics for big data processing?

A) They provide a scalable and distributed environment for processing large datasets
B) They offer more security features
C) They are easier to configure
D) They consume fewer resources

Answer: A) They provide a scalable and distributed environment for processing large datasets

67. How can you integrate Synapse Analytics with other Azure services like Power BI and Azure ML?

A) By using Synapse Studio connectors and integration features
B) By using SQL triggers
C) By creating separate databases
D) By encrypting the data

Answer: A) By using Synapse Studio connectors and integration features

68. What method can be used to ensure data quality in Synapse Analytics before analysis?

A) Using Data Flows for data cleansing and transformation
B) Using SQL scripts
C) By increasing database size
D) By storing data in Blob Storage

Answer: A) Using Data Flows for data cleansing and transformation

69. How can you implement a data lakehouse architecture using Synapse Analytics?

A) By combining Synapse SQL pools with Azure Data Lake Storage and Synapse Pipelines
B) By using SQL triggers
C) By creating new databases
D) Using Azure Functions

Answer: A) By combining Synapse SQL pools with Azure Data Lake Storage and Synapse Pipelines

70. What is the role of Synapse Studio in a data analytics workflow?

A) It provides an integrated workspace for data exploration, preparation, management, and visualization
B) It monitors network configuration
C) It configures database encryption
D) It manages SQL triggers

Answer: A) It provides an integrated workspace for data exploration, preparation, management, and visualization

71. How can a healthcare company use Synapse Analytics to support population health management?

A) By integrating with electronic health records (EHR) and using machine learning models for predictive analytics
B) By exporting data to Excel
C) Using SQL triggers
D) By creating new databases

Answer: A) By integrating with electronic health records (EHR) and using machine learning models for predictive analytics

72. How can Synapse Analytics help in real-time customer sentiment analysis for a retail company?

A) By using Spark Streaming with Synapse Pipelines to analyze social media and customer feedback data
B) By using SQL scripts
C) By creating separate databases
D) By storing data in Blob Storage

Answer: A) By using Spark Streaming with Synapse Pipelines to analyze social media and customer feedback data

73. What is the best approach to handle data archiving and retention in Synapse Analytics?

A) Implementing tiered storage options and lifecycle policies
B) By using SQL triggers
C) By increasing database size
D) By creating new databases

Answer: A) Implementing tiered storage options and lifecycle policies

74. How can you secure Synapse Analytics against unauthorized access and data breaches?

A) By implementing Azure Active Directory integration and role-based access control (RBAC)
B) By using SQL scripts
C) By creating separate databases
D) By using Azure Logic Apps

Answer: A) By implementing Azure Active Directory integration and role-based access control (RBAC)

75. How can a logistics company optimize route planning and delivery schedules using Synapse Analytics?

A) By integrating with Azure Machine Learning to develop predictive models
B) By exporting data to a third-party tool
C) Using SQL triggers
D) By creating separate databases

Answer: A) By integrating with Azure Machine Learning to develop predictive models

76. What feature in Synapse Analytics helps in managing data schema changes and version control?

A) Schema management tools in Synapse Studio
B) Using SQL triggers
C) By creating new databases
D) Using Power Automate

Answer: A) Schema management tools in Synapse Studio

77. How can you perform sentiment analysis on customer reviews stored in Synapse Analytics?

A) By using Azure Cognitive Services text analytics integrated with Synapse Pipelines
B) By exporting data to Excel
C) Using SQL scripts
D) By creating separate databases

Answer: A) By using Azure Cognitive Services text analytics integrated with Synapse Pipelines

78. How can Synapse Analytics support large-scale data migration from on-premises systems?

A) By using Azure Data Migration Service and Synapse Pipelines
B) By using SQL triggers
C) By creating new databases
D) By using Azure Functions

Answer: A) By using Azure Data Migration Service and Synapse Pipelines

79. How can an e-commerce company personalize customer experiences using Synapse Analytics?

A) By leveraging Synapse SQL and machine learning models to analyze customer behavior and preferences
B) By exporting data to a third-party tool
C) Using SQL scripts
D) By creating separate databases

Answer: A) By leveraging Synapse SQL and machine learning models to analyze customer behavior and preferences

80. What is the role of Data Flows in Synapse Analytics?

A) To provide a visual interface for designing data transformation logic
B) To manage SQL triggers
C) To configure database encryption
D) To monitor network configuration

Answer: A) To provide a visual interface for designing data transformation logic

81. How can a company use Synapse Analytics to implement a single source of truth for their data?

A) By centralizing data from various sources into a Synapse data warehouse and applying data governance practices
B) By using SQL triggers
C) By creating separate databases
D) By exporting data to Excel

Answer: A) By centralizing data from various sources into a Synapse data warehouse and applying data governance practices

82. What is the purpose of integrating Synapse Analytics with Azure Purview?

A) To enhance data cataloging, governance, and lineage tracking
B) To manage SQL triggers
C) To configure database encryption
D) To monitor network configuration

Answer: A) To enhance data cataloging, governance, and lineage tracking

83. How can Synapse Analytics help a media company analyze viewer engagement data?

A) By using Synapse SQL and Spark pools to process and analyze large volumes of viewer data
B) By exporting data to a third-party tool
C) Using SQL triggers
D) By creating separate databases

Answer: A) By using Synapse SQL and Spark pools to process and analyze large volumes of viewer data

84. What is the advantage of using dedicated SQL pools over serverless SQL pools in Synapse Analytics?

A) Dedicated SQL pools provide better performance for high concurrency and complex queries
B) Serverless SQL pools offer better security features
C) Dedicated SQL pools consume fewer resources
D) Serverless SQL pools are easier to configure

Answer: A) Dedicated SQL pools provide better performance for high concurrency and complex queries

85. How can a financial institution detect anomalies in transaction data using Synapse Analytics?

A) By integrating with Azure Machine Learning for anomaly detection models
B) By exporting data to a third-party tool
C) Using SQL triggers
D) By creating separate databases

Answer: A) By integrating with Azure Machine Learning for anomaly detection models

86. What is the best practice for loading historical data into Synapse Analytics for analysis?

A) Using PolyBase or COPY statement to load large volumes of data efficiently
B) By using SQL scripts
C) By creating new databases
D) By encrypting the data

Answer: A) Using PolyBase or COPY statement to load large volumes of data efficiently

87. How can Synapse Analytics help in optimizing supply chain operations for a manufacturing company?

A) By analyzing production and logistics data using Synapse SQL and machine learning models
B) By exporting data to Excel
C) Using SQL scripts
D) By creating separate databases

Answer: A) By analyzing production and logistics data using Synapse SQL and machine learning models

88. How can you automate the backup and recovery of Synapse Analytics data?

A) By using Azure Backup and Recovery solutions
B) Using SQL scripts
C) By creating new databases
D) Using Power Automate

Answer: A) By using Azure Backup and Recovery solutions

89. How can a company ensure their Synapse Analytics data warehouse meets compliance requirements?

A) By implementing data encryption, access control, and audit logging
B) By using SQL triggers
C) By creating separate databases
D) By exporting data to a third-party tool

Answer: A) By implementing data encryption, access control, and audit logging

90. How can Synapse Analytics be used to support customer segmentation and targeting for a marketing campaign?

A) By using Synapse SQL to analyze customer data and identify segments based on behavior and demographics
B) By exporting data to Excel
C) Using SQL scripts
D) By creating separate databases

Answer: A) By using Synapse SQL to analyze customer data and identify segments based on behavior and demographics

91. What is the benefit of using Synapse Studio for collaborative data analytics projects?

A) It provides a unified workspace for multiple users to collaborate on data preparation, management, and analysis
B) It monitors network configuration
C) It manages SQL triggers
D) It configures database encryption

Answer: A) It provides a unified workspace for multiple users to collaborate on data preparation, management, and analysis

92. How can Synapse Analytics be integrated with third-party BI tools for advanced reporting?

A) By using data connectors and APIs to link Synapse data with BI tools like Tableau or Qlik
B) By exporting data to Excel
C) Using SQL scripts
D) By creating separate databases

Answer: A) By using data connectors and APIs to link Synapse data with BI tools like Tableau or Qlik

93. How can a retail company use Synapse Analytics to optimize inventory management?

A) By analyzing sales and inventory data to predict demand and optimize stock levels
B) By exporting data to a third-party tool
C) Using SQL triggers
D) By creating separate databases

Answer: A) By analyzing sales and inventory data to predict demand and optimize stock levels

94. What feature in Synapse Analytics allows you to schedule and automate data processing tasks?

A) Synapse Pipelines
B) SQL Server Management Studio
C) Azure Monitor
D) Power Automate

Answer: A) Synapse Pipelines

95. How can a company use Synapse Analytics to perform cross-regional data analysis?

A) By setting up data replication and using Synapse SQL to query data from different regions
B) By using SQL triggers
C) By creating separate databases
D) By exporting data to Excel

Answer: A) By setting up data replication and using Synapse SQL to query data from different regions

96. How can you improve query performance in Synapse Analytics when dealing with large datasets?

A) By optimizing distribution keys and creating columnstore indexes
B) By increasing database size
C) Using SQL triggers
D) By creating separate databases

Answer: A) By optimizing distribution keys and creating columnstore indexes

97. How can Synapse Analytics support predictive maintenance for industrial equipment?

A) By integrating with IoT data sources and using machine learning models for predictive analytics
B) By exporting data to Excel
C) Using SQL scripts
D) By creating separate databases

Answer: A) By integrating with IoT data sources and using machine learning models for predictive analytics

98. What is the best practice for managing large-scale data transformations in Synapse Analytics?

A) Using Data Flows and Spark pools for efficient data processing
B) By using SQL triggers
C) By increasing database size
D) By creating new databases

Answer: A) Using Data Flows and Spark pools for efficient data processing

99. How can Synapse Analytics help in developing a 360-degree view of the customer?

A) By consolidating data from various sources and using analytics to provide insights into customer behavior
B) By exporting data to a third-party tool
C) Using SQL scripts
D) By creating separate databases

Answer: A) By consolidating data from various sources and using analytics to provide insights into customer behavior

100. How can Synapse Analytics be used to support real-time business intelligence for an e-commerce platform?

A) By integrating with streaming data sources and using serverless SQL pools for real-time querying
B) By using SQL triggers
C) By creating separate databases
D) By exporting data to Excel

Answer: A) By integrating with streaming data sources and using serverless SQL pools for real-time querying