Cosmos DB Interview Questions and Answers for 5 years experience

1. What are the core differences between Cosmos DB's API for SQL and API for MongoDB?

   • Answer: The SQL API uses a relational model with schemas and SQL queries, while the MongoDB API uses a NoSQL document model with flexible schemas and JSON-like documents. SQL API offers ACID transactions and strong consistency, whereas MongoDB API prioritizes flexibility and scalability, often utilizing eventual consistency. Query languages and data modeling approaches differ significantly, impacting application design choices.

2. Explain the concept of consistency levels in Cosmos DB and when you would choose each.

   • Answer: Cosmos DB offers several consistency levels (Strong, Bounded Staleness, Session, Consistent Prefix, Eventual). Strong consistency guarantees all reads see the most up-to-date data, ideal for financial transactions. Bounded Staleness allows a small delay for reads, balancing consistency and performance. Session consistency ensures data consistency within a single session, suitable for interactive applications. Consistent Prefix guarantees consistency within a partition key range, improving read performance. Eventual consistency prioritizes high availability and write performance, accepting potential data discrepancies, suitable for less critical data.

3. How do you handle schema changes in Cosmos DB's SQL API and MongoDB API?

   • Answer: In the SQL API, schema changes require careful planning and often involve altering table definitions. In the MongoDB API, schema changes are handled implicitly since documents are schemaless. Adding new fields is straightforward; however, you need to handle potential backward compatibility issues if you remove fields or alter existing ones. Proper versioning and data migration strategies are crucial in both APIs.

4. Describe the different indexing options in Cosmos DB and how to choose the right one.

   • Answer: Cosmos DB offers various indexing options, including automatic indexing (default), manual indexing (specifying specific indexes), and composite indexes (combining multiple fields). Automatic indexing simplifies initial setup but may not be optimal for performance. Manual indexing allows precise control but requires careful planning. Composite indexes enhance query performance for complex queries involving multiple fields. The choice depends on query patterns, data access frequency, and performance requirements.

5. Explain the concept of partitions and partition keys in Cosmos DB. Why are they important?

   • Answer: Partitions divide a large dataset into smaller, manageable units, improving scalability and performance. The partition key determines how data is distributed across partitions. Choosing an appropriate partition key is critical for performance because it impacts query efficiency and throughput. A poorly chosen partition key can lead to performance bottlenecks as all queries targeting a specific partition must go through a single partition.

6. How do you handle data replication and failover in Cosmos DB?

   • Answer: Cosmos DB automatically handles data replication and failover across multiple regions for high availability and disaster recovery. Data is replicated synchronously or asynchronously depending on the chosen consistency level. If a region fails, Cosmos DB automatically switches to a healthy region, ensuring minimal downtime. The process is transparent to the application.

7. What are throughput and RU/s in Cosmos DB? How do they affect your application design?

   • Answer: Throughput in Cosmos DB is measured in Request Units per second (RU/s). RU/s represents the processing power allocated to a container or database. Higher RU/s means greater read/write capacity. Application design must consider the required RU/s based on expected workload and query patterns. Insufficient RU/s can lead to performance bottlenecks and request throttling. You need to monitor RU consumption and adjust provisioning as needed.

8. Describe different ways to perform data backups and restores in Cosmos DB.

   • Answer: Cosmos DB offers automatic backups by default. You can also utilize Azure Backup service for more granular control and offsite backups. For restores, you can leverage these automatic backups or restore from point-in-time backups. The restoration method depends on your RPO (Recovery Point Objective) and RTO (Recovery Time Objective) requirements.

9. Explain how to use change feed in Cosmos DB and its use cases.

   • Answer: The change feed captures a continuous stream of document changes in a Cosmos DB container. Applications can subscribe to this feed to process changes in real-time or near real-time. Use cases include building real-time dashboards, implementing event sourcing, creating audit trails, and enabling change data capture (CDC) for downstream systems.

10. How do you monitor and troubleshoot performance issues in Cosmos DB?

    • Answer: Cosmos DB provides monitoring tools and metrics to track RU consumption, latency, request throttling, and other performance indicators. Azure Monitor and the Cosmos DB portal offer dashboards and alerts to identify potential issues. Troubleshooting involves analyzing these metrics, examining query plans, checking indexing strategies, and optimizing partition key selection. Analyzing slow queries and improving query performance is a crucial part of troubleshooting.

11. Discuss different ways to optimize queries in Cosmos DB.

    • Answer: Query optimization involves using appropriate indexing strategies, choosing efficient query patterns, leveraging partition keys effectively, using predicates to filter data at the source, and avoiding unnecessary operations. Understanding query execution plans helps identify bottlenecks and improve performance. Using stored procedures for complex operations can also improve efficiency.

12. Explain the concept of time-to-live (TTL) in Cosmos DB.

    • Answer: TTL allows automatic deletion of documents after a specified period. This is useful for managing data lifecycle, automatically removing outdated or obsolete data, and reducing storage costs. It’s set at the container level and applied to individual documents based on a specified field.

13. How would you design a Cosmos DB solution for a high-volume, low-latency application?

    • Answer: This requires careful consideration of partition keys, indexing, consistency levels, and RU/s provisioning. A well-designed partition key is crucial to distribute the load. Proper indexing and query optimization are essential for low latency. Choosing a suitable consistency level (potentially Bounded Staleness) balances consistency and performance. Provisioning ample RU/s prevents request throttling. Horizontal scaling through adding more partitions is key to handling high volume.

14. Compare and contrast Cosmos DB with other NoSQL databases like Cassandra and MongoDB.

    • Answer: Cosmos DB offers multi-model capabilities, supporting SQL, MongoDB, Gremlin, and Cassandra APIs. Cassandra focuses on high availability and scalability with a strong emphasis on eventual consistency. MongoDB is a popular document database offering flexibility and scalability. Cosmos DB distinguishes itself through its global distribution and serverless options, alongside its multi-model nature, providing a wider range of use cases. The best choice depends on specific needs and priorities.

15. Explain how to integrate Cosmos DB with other Azure services.

    • Answer: Cosmos DB integrates seamlessly with numerous Azure services like Azure Functions, Logic Apps, Event Hubs, and Stream Analytics. Data can be ingested from and exported to other services using various techniques, including change feed integration, Azure Data Factory, and Azure Synapse Analytics. The choice of integration method depends on the specific requirements and data flow patterns.

16. How do you handle security in Cosmos DB?

    • Answer: Cosmos DB utilizes Azure Active Directory for authentication and authorization, providing role-based access control (RBAC) to manage permissions. Network security is controlled via virtual networks (VNets) and firewalls. Data encryption is managed using encryption keys and encryption at rest.

17. Describe your experience with using Cosmos DB's serverless option.

    • Answer: (This answer will vary based on experience, but should discuss cost savings, automatic scaling, and the trade-offs involved, such as potential for slightly higher latency compared to provisioned throughput.)

18. How do you handle large-scale data migrations to Cosmos DB?

    • Answer: Large-scale migrations require a phased approach, potentially involving Azure Data Factory, tools like Azure Data Box, and careful planning of data transformation. Data validation and monitoring are critical throughout the process. Testing and validating the migration in stages is crucial to minimize disruption.

19. What are some common performance anti-patterns in Cosmos DB?

    • Answer: Common anti-patterns include poorly chosen partition keys, lack of appropriate indexing, inefficient queries (e.g., using wildcard searches extensively), overuse of cross-partition queries, and insufficient RU/s provisioning.

20. How do you handle data consistency conflicts in Cosmos DB?

    • Answer: Conflict resolution depends on the consistency level. Strong consistency prevents conflicts. Other consistency levels might require application-level conflict resolution mechanisms using ETags or Last-Write-Wins strategies. Proper error handling and retry mechanisms are important in handling potential conflicts.

21. Explain your experience with using stored procedures in Cosmos DB.

    • Answer: (This answer will vary based on experience but should describe scenarios where stored procedures were used for complex operations, improved performance, or data validation. It should also mention potential downsides like debugging complexity.)

22. How do you ensure data integrity in Cosmos DB?

    • Answer: Data integrity is ensured through proper schema design, data validation at the application level, and potentially using stored procedures for complex validation rules. Monitoring and alerting help detect data anomalies. Regular backups and disaster recovery planning are essential.

23. Describe your experience working with different Cosmos DB APIs (SQL, MongoDB, Gremlin, Cassandra).

    • Answer: (This answer will vary depending on the candidate's experience. It should highlight specific projects and the reasons for choosing a particular API.)

24. How would you approach designing a sharding strategy for a Cosmos DB database?

    • Answer: A good sharding strategy involves choosing an appropriate partition key that evenly distributes data across partitions. Careful consideration of data access patterns and query workload is crucial. Monitoring and adjusting the sharding strategy as data grows is important for maintaining performance.

25. What are some best practices for designing Cosmos DB containers?

    • Answer: Best practices include choosing appropriate partition keys, defining proper indexing strategies, selecting a suitable consistency level, considering throughput requirements, and designing a schema that meets the application's needs efficiently.

26. Explain your understanding of Cosmos DB's global distribution capabilities.

    • Answer: Global distribution allows for data replication across multiple regions, improving availability, latency for geographically dispersed users, and disaster recovery. It allows for managing data residency compliance and improved performance.

27. How would you troubleshoot a Cosmos DB query that is performing poorly?

    • Answer: I would start by examining the query plan in the Cosmos DB portal to identify bottlenecks. I would then check the indexing strategy, ensuring appropriate indexes exist for the query. I would review the partition key strategy to ensure data is distributed effectively. Finally, I would investigate whether the query could be optimized by using more efficient filtering or other techniques.

28. Describe your experience using Azure CLI or PowerShell to manage Cosmos DB resources.

    • Answer: (This answer will vary based on experience. It should demonstrate familiarity with using command-line tools for tasks like creating containers, managing resources, and automating tasks.)

29. How would you design a Cosmos DB solution for handling geographically distributed data?

    • Answer: This requires utilizing Cosmos DB's global distribution capabilities. Data is replicated across multiple regions closest to users, minimizing latency. Careful consideration of data sovereignty and compliance requirements is crucial. Appropriate consistency levels and strategies for managing data consistency across regions must be considered.

30. What are the different ways to handle data updates in Cosmos DB?

    • Answer: Updates can be performed using replace, upsert, or partial updates. The approach depends on whether the entire document needs to be replaced or only specific fields modified. Using ETags helps manage concurrency and avoid conflicts.

31. Explain your understanding of Cosmos DB's pricing model.

    • Answer: Cosmos DB uses a consumption-based model, charging based on provisioned throughput (RU/s) and storage used. Serverless options offer a pay-per-request model. Understanding the trade-offs between provisioned and serverless options is key to cost optimization.

32. How would you handle schema evolution in a large Cosmos DB application?

    • Answer: This requires a well-defined schema migration plan, potentially involving adding new fields gradually, handling backward compatibility, and using versioning mechanisms to track changes. Thorough testing is essential to avoid data corruption or application errors.

33. What are some tools and techniques you use for performance testing of Cosmos DB applications?

    • Answer: Tools like JMeter or k6 can be used to simulate various load scenarios. Monitoring RU consumption, latency, and error rates during testing helps identify performance bottlenecks. Analyzing query execution plans is essential to understand query performance.

34. Describe your experience working with Cosmos DB's SDKs for different programming languages (e.g., .NET, Java, Node.js).

    • Answer: (This answer will vary based on experience but should highlight familiarity with using different SDKs for various tasks, such as CRUD operations, query execution, and handling asynchronous operations.)

35. Explain your experience with using triggers in Cosmos DB.

    • Answer: (This answer will vary based on experience but should demonstrate understanding of pre- and post-triggers, their use cases, and how they integrate with application logic.)

36. How do you approach capacity planning for a Cosmos DB database?

    • Answer: Capacity planning involves estimating the expected workload, considering factors like read/write operations, query patterns, data volume, and expected growth. It's important to monitor RU/s consumption to adjust provisioning as needed. Serverless options can provide automatic scaling, simplifying capacity management.

37. What are the different types of containers available in Cosmos DB?

    • Answer: There are several container types, depending on the chosen API. For the SQL API, there are standard containers. The MongoDB API uses collections, while the Gremlin API uses graphs.

38. Explain your experience with using Cosmos DB's analytical capabilities.

    • Answer: (This answer will vary based on experience but might include using change feed integration with analytical tools or exporting data to other analytical services like Azure Synapse Analytics.)

39. How would you design a Cosmos DB solution for a multi-tenant application?

    • Answer: This involves careful partition key design to isolate data for different tenants. It requires robust access control mechanisms using RBAC to manage tenant isolation and data security. Consider using different databases or containers to completely isolate tenants for higher security.

40. Describe your experience with monitoring and alerting in Cosmos DB.

    • Answer: (This answer will vary, but should discuss using Azure Monitor, setting up alerts for critical metrics like RU consumption, latency, and errors. It should also show an understanding of how to interpret monitoring data and take corrective action.)

41. How do you handle data versioning in Cosmos DB?

    • Answer: Data versioning can be implemented using ETags or adding a version field to the documents. This helps track changes and resolve conflicts. Proper versioning is important for maintaining data integrity.

42. What are the considerations for choosing between provisioned throughput and serverless in Cosmos DB?

    • Answer: Provisioned throughput offers predictable performance at a potentially higher cost. Serverless is cost-effective for bursty workloads but might have slightly higher latency. The choice depends on the application's performance requirements and cost constraints.

43. How do you handle data deletion in Cosmos DB, considering factors like compliance and recovery?

    • Answer: Data deletion should follow a structured process, potentially involving soft deletes (marking records as deleted rather than physically deleting them), retention policies, and compliance requirements. Regular backups ensure data recovery if needed.

44. Describe a challenging Cosmos DB project you worked on and how you overcame the challenges.

    • Answer: (This is a behavioral question and requires a detailed response illustrating problem-solving skills and technical expertise. Focus on a specific project, outlining the challenges, the steps taken to address them, and the successful outcome.)

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