Cosmos DB Interview Questions & Answers

Cosmos DB Overview:

1. What is Azure Cosmos DB?

   • Azure Cosmos DB is a globally distributed, multi-model database service for building highly responsive and scalable applications.

2. What are the key features of Azure Cosmos DB?

   • Multi-model support, global distribution, automatic and instant scalability, multiple consistency models, and comprehensive SLAs.

3. Explain the multi-model capabilities of Azure Cosmos DB.

   • Cosmos DB supports multiple data models, including DocumentDB (JSON documents), Graph (graph data), Table (key-value pairs), Column-Family (column-family data), and Gremlin (graph traversal).

4. How does global distribution work in Azure Cosmos DB?

   • Azure Cosmos DB enables global distribution by replicating data across multiple Azure regions, allowing low-latency access to data from any location.

5. What are the consistency models available in Azure Cosmos DB?

   • Azure Cosmos DB supports five consistency levels: Strong, Bounded staleness, Session, Consistent Prefix, and Eventual consistency.

Data Modeling in Cosmos DB:

6. Explain the concept of partitions in Azure Cosmos DB.

   • Partitions are logical divisions of data within a container in Cosmos DB. Each partition can be managed independently, allowing for horizontal scalability.

7. How is data stored in Azure Cosmos DB?

   • Data is stored as JSON documents within containers. Each document has a unique identifier (_id) and is part of a collection or container.

8. What is the purpose of the RU/s (Request Units per second) in Cosmos DB?

   • RU/s represents the throughput capacity in Azure Cosmos DB, indicating the number of read and write operations that can be performed per second.

9. Explain the indexing options in Azure Cosmos DB.

   • Cosmos DB supports automatic indexing by default, but you can customize indexing policies based on your application's needs.

10. How can you optimize queries in Cosmos DB?

    • Optimize queries by using the right indexing, partitioning, and utilizing the built-in query optimization features.

Querying and Transactions in Cosmos DB:

11. What is the SQL-like query language used in Cosmos DB?

    • Cosmos DB uses the SQL API, allowing developers to write SQL-like queries for data retrieval.

12. How can you perform transactions in Azure Cosmos DB?

    • Cosmos DB provides transactional consistency for single-partition transactions. Multi-partition transactions are supported through the use of stored procedures.

13. Explain the concept of stored procedures in Cosmos DB.

    • Stored procedures are JavaScript functions that are executed as transactions on the server side, allowing for complex operations and multi-document transactions.

14. What is the role of the Change Feed in Cosmos DB?

    • The Change Feed provides a continuous feed of changes to documents in a container, enabling scenarios like real-time updates and data synchronization.

15. How does the cross-document JOIN work in Cosmos DB?

    • Cosmos DB supports cross-document JOIN using the SQL API, allowing for queries that involve multiple documents.

Partitioning Strategies:

16. Why is partitioning important in Cosmos DB?

    • Partitioning is crucial for achieving scalability in Cosmos DB. It allows data to be distributed across multiple physical partitions for parallel processing.

17. What are the key considerations when choosing a partition key?

    • Choose a partition key that evenly distributes data, has high cardinality, and is frequently used in queries to ensure efficient performance.

18. How does Cosmos DB handle hot partition issues?

    • Hot partition issues can be mitigated by choosing a partition key that evenly distributes data and avoids concentrating high read or write workloads on a single partition.

Scaling and Performance Optimization:

19. How can you scale throughput in Azure Cosmos DB?

    • You can scale throughput by adjusting the RU/s provisioned for a container or by using the autoscale option to automatically adjust throughput based on demand.

20. Explain the benefits of using the Azure Cosmos DB emulator for development.

    • The emulator allows developers to test and develop applications locally without incurring costs associated with using the Azure Cosmos DB service.

21. How does indexing impact performance in Cosmos DB?

    • Proper indexing is crucial for efficient query performance. Over-indexing can consume additional RU/s, while under-indexing may result in slower queries.

Cosmos DB Security:

22. What security features does Azure Cosmos DB provide?

    • Cosmos DB offers features such as network security, data encryption, Azure AD-based authentication, role-based access control (RBAC), and Virtual Network Service Endpoints.

23. How can you enable encryption for data at rest in Cosmos DB?

    • Data at rest encryption is enabled by default in Cosmos DB. It uses Azure Storage Service Encryption (SSE) to encrypt data stored on physical media.

Cosmos DB Monitoring and Optimization:

24. What tools are available for monitoring Cosmos DB performance?

    • Azure Portal, Azure Monitor, and Azure Cosmos DB Profiler are tools that can be used for monitoring and optimizing Cosmos DB performance.

25. Explain the purpose of the Azure Cosmos DB Profiler.

    • The Azure Cosmos DB Profiler is a tool that captures and analyzes diagnostic information to identify and resolve performance bottlenecks.

Cosmos DB Indexing and Query Optimization:

26. How can you optimize the performance of queries in Cosmos DB?

    • Optimize queries by using appropriate indexing, reducing unnecessary properties in query projections, and utilizing partition keys efficiently.

27. What are the considerations for choosing between a single-partition and multi-partition collection in Cosmos DB?

    • Choose a single-partition collection for small datasets or scenarios where a single partition key is sufficient. Use a multi-partition collection for larger datasets that require horizontal scaling.

Cosmos DB Consistency Models:

28. Explain the differences between strong consistency and eventual consistency in Cosmos DB.

    • Strong consistency ensures that all reads reflect the most recent write, while eventual consistency allows for eventual convergence of replicas but may return intermediate results.

29. How can you choose the right consistency level for your Cosmos DB application?

    • Choose the consistency level based on the specific requirements of your application, considering factors such as data freshness, performance, and scalability.

Data Migration and Import/Export:

30. What tools are available for data migration to Cosmos DB?

    • Azure Data Factory, Azure Cosmos DB Data Migration tool, and Azure Cosmos DB SDKs are commonly used for data migration to Cosmos DB.

31. Explain the process of exporting data from Cosmos DB.

    • Data can be exported from Cosmos DB using the Azure Portal, Azure CLI, or programmatically using the Cosmos DB SDKs.

Cosmos DB Server-Side Programming:

32. What are the options for server-side programming in Cosmos DB?

    • Server-side programming in Cosmos DB can be done using stored procedures, triggers, and user-defined functions (UDFs) written in JavaScript.

33. How does Cosmos DB handle stored procedure transactions?

    • Stored procedures in Cosmos DB are executed as transactions, allowing multiple operations to be performed atomically.

Cosmos DB Triggers and User-Defined Functions (UDFs):

34. What are triggers in Cosmos DB?
    • Triggers in Cosmos DB are server-side

scripts that can be executed before or after a document is inserted, updated, or deleted.

35. Explain the purpose of User-Defined Functions (UDFs) in Cosmos DB.
    • UDFs are reusable JavaScript functions in Cosmos DB that can be used to encapsulate logic for queries, transformations, or aggregations.

Cosmos DB Gremlin API:

36. What is the Gremlin API in Cosmos DB?

    • The Gremlin API in Cosmos DB supports graph data models and the Gremlin query language for graph traversals.

37. How can you create and query graphs in Cosmos DB using the Gremlin API?

    • Create graphs by defining vertices and edges, and query graphs using the Gremlin query language for traversals.

Cosmos DB Time-to-Live (TTL):

38. What is Time-to-Live (TTL) in Cosmos DB?
    • TTL is a feature in Cosmos DB that allows documents to be automatically deleted after a specified duration, which is useful for managing data retention.

Cosmos DB Change Feed:

39. How does the Change Feed feature in Cosmos DB work?
    • The Change Feed provides a continuous feed of changes to documents in a container, allowing applications to react to updates in real-time.

Cosmos DB Geospatial Queries:

40. What is the significance of geospatial queries in Cosmos DB?

    • Cosmos DB supports geospatial indexing and queries, allowing for efficient retrieval of data based on geographic locations.

41. How can you perform a geospatial query in Cosmos DB?

    • Geospatial queries can be performed using the ST_DISTANCE function to find points within a specified distance of a reference point.

Cosmos DB Backup and Restore:

42. How can you perform backups in Cosmos DB?

    • Cosmos DB automatically takes backups as part of its managed service. Additionally, you can use the Azure Portal or APIs for on-demand backups.

43. Explain the process of restoring data in Cosmos DB.

    • Data can be restored by selecting a specific point in time from the available backups using the Azure Portal or APIs.

Cosmos DB Indexing Policies:

44. What considerations should be taken into account when configuring indexing policies in Cosmos DB?
    • Consider factors such as query patterns, data size, and read/write workloads when configuring indexing policies to optimize performance.

Cosmos DB Monitoring and Alerts:

45. How can you monitor the performance of Cosmos DB?

    • Azure Monitor provides metrics and logs for monitoring Cosmos DB performance. Additionally, you can use the Azure Portal and Azure CLI.

46. What types of alerts can be configured for Cosmos DB?

    • Alerts can be configured for metrics such as RU/s consumption, storage usage, and availability.

Cosmos DB Resource Management:

47. How can you manage and monitor the utilization of Cosmos DB resources?
    • Utilize the Azure Portal, Azure CLI, and Azure PowerShell to monitor and manage Cosmos DB resources such as containers, databases, and throughput.

Cosmos DB Time Series Data:

48. How can Cosmos DB be used for managing time series data?
    • Cosmos DB is suitable for time series data by leveraging its scalable architecture and support for efficient querying of time-based data.

Cosmos DB Best Practices:

49. What are some best practices for designing and deploying applications with Cosmos DB?

    • Consider factors such as partitioning, indexing, consistency levels, and throughput scaling based on the specific needs of your application.

50. How can you troubleshoot performance issues in Cosmos DB?

    • Monitor metrics, analyze query performance, review indexing policies, and use tools like the Azure Cosmos DB Profiler to identify and troubleshoot performance issues.