Amazon DynamoDB Interview Questions and Answers

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Q: What are the key features of Amazon DynamoDB?

Answer: Key features of Amazon DynamoDB include:

• Fully managed: AWS handles administrative tasks such as hardware provisioning, setup, configuration, monitoring, and scaling.
• Scalable: DynamoDB automatically scales workloads by partitioning data across multiple servers to handle growing demands.
• Performance: Provides consistent single-digit millisecond latency for read and write operations.
• Flexible data model: Supports both document and key-value data models using JSON-like syntax.
• Built-in security: Offers encryption at rest and in transit, fine-grained access control, and integration with AWS Identity and Access Management (IAM).

Q: What are the different types of primary keys supported by DynamoDB?

Answer: DynamoDB supports two types of primary keys:

• Partition key (hash key): A single attribute used to determine the partition where the item is stored.
• Composite primary key (hash-and-range key): Comprises a partition key and a sort key. This key determines both the partition and the sort order of the items.

Q: How does DynamoDB ensure scalability and high availability?

Answer: DynamoDB ensures scalability and high availability through:

• Partitioning: Automatically partitions data across multiple servers using the partition key, enabling DynamoDB to handle high traffic volumes efficiently.
• Replication: Data is synchronously replicated across multiple availability zones within a region, ensuring fault tolerance and high availability.

Q: What is the difference between provisioned throughput and on-demand capacity modes in DynamoDB?

Provisioned throughput mode:

• Allows you to specify the read and write capacity units (RCUs and WCUs) required for the table upfront.
• You are billed based on the provisioned capacity, irrespective of actual usage.

On-demand capacity mode:

• Automatically scales read and write capacity based on actual usage.
• You are charged per request based on the capacity consumed.

Q: How does DynamoDB handle consistency?

Answer: DynamoDB offers two consistency models:

• Eventual consistency: Prioritizes availability over consistency. Data across copies is eventually consistent within seconds.
• Strong consistency: Guarantees immediate consistency for read operations, ensuring the most up-to-date data is returned.

Q: What are DynamoDB streams?

Answer: DynamoDB streams capture a time-ordered sequence of item-level modifications (inserts, updates, deletes) in a DynamoDB table.

• Use cases include triggering AWS Lambda functions, replicating data across tables or regions, and implementing cross-region replication.

Q: How can you monitor and manage DynamoDB?

Answer: DynamoDB provides several tools for monitoring and management:

• Amazon CloudWatch: Monitors metrics like read/write capacity utilization, error rates, and throttling events.
• DynamoDB Console: Offers a graphical interface for managing tables, monitoring performance, and configuring settings.
• AWS CLI and SDKs: Enable programmatic access to DynamoDB for automation, scripting, and integration with other AWS services.

Q: What are some best practices for designing DynamoDB tables?

Answer: Best practices include:

• Selecting an appropriate partition key to ensure even data distribution and avoid hot partitions.
• Using sparse indexes to minimize storage costs and improve query performance.
• Leveraging secondary indexes and materialized views to support various access patterns.
• Choosing optimal data types to reduce storage space and improve performance.
• Implementing DynamoDB streams for real-time data processing and change capture.

Q: How does DynamoDB encryption work?

Answer: DynamoDB provides encryption at rest using AWS Key Management Service (KMS).

• All data, including backups and replicas, is encrypted using either an AWS-managed key or a customer-managed key.
• This ensures data protection and compliance with security requirements.

Q: What are the use cases for DynamoDB?

Answer: DynamoDB is ideal for:

• Web and mobile applications: Provides low-latency performance for user authentication, session storage, and activity tracking.
• Gaming applications: Tracks player progress, leaderboards, and real-time interactions.
• IoT applications: Handles time-series data, device tracking, and event logging.
• E-commerce: Manages product catalogs, shopping carts, and order processing.
• Serverless applications: Integrates seamlessly with AWS Lambda for event-driven architectures.

Q: What are global secondary indexes (GSIs) and local secondary indexes (LSIs) in DynamoDB?

Global Secondary Index (GSI):

• Allows queries on non-primary key attributes.
• Can have a different partition key and sort key than the base table.
• Data is stored in a separate table partition from the base table.

Local Secondary Index (LSI):

• Enables querying on an alternate sort key for the same partition key as the base table.
• Shares the same partition key as the base table and is limited to 10 GB of data per partition.

Q: What is a DynamoDB Accelerator (DAX)?

• DAX is a fully managed in-memory cache for DynamoDB that improves response times for read-heavy workloads.
• Key features:
• Reduces latency for eventually consistent reads to microseconds.
• Eliminates the need to manage a separate caching layer.
• Fully integrated with DynamoDB, maintaining compatibility with existing applications.

Q: What is the role of AWS Lambda in DynamoDB workflows?

AWS Lambda can be integrated with DynamoDB to:

• Process DynamoDB streams for real-time event-driven architectures.
• Perform data transformations or aggregations based on changes in a table.
• Implement custom business logic when changes occur in the database.
• Enable workflows like notifications, analytics, and cross-region replication.

Q: How does DynamoDB handle large items or datasets?

• DynamoDB imposes a maximum item size of 400 KB, including all attributes.
• For larger datasets, consider:
• Splitting data across multiple items.
• Using S3 for storing large objects and storing references (keys or URLs) in DynamoDB.
• Using batch writes and queries to process large datasets efficiently.

Q: What are DynamoDB transactions?

DynamoDB supports ACID-compliant transactions for applications requiring:

• Multiple-item operations: Perform transactional updates, inserts, or deletes across multiple items or tables.
• Atomicity and consistency: Ensures either all operations succeed or none are applied.
• Example use cases: Financial systems, inventory management, and complex workflows that need data integrity.

Q: What are the limitations of DynamoDB?

While DynamoDB is powerful, it has some limitations:

• Item size: Maximum size is 400 KB.
• LSI constraints: Maximum of five LSIs per table and 10 GB storage per partition key.
• Complex queries: Limited compared to traditional relational databases; requires careful schema design.
• Cost: Can be expensive for write-heavy or unpredictable workloads.
• Strict throughput limits: Misconfigured RCUs/WCUs can lead to throttling.

Q: How does DynamoDB support backup and restore?

DynamoDB provides on-demand backups and point-in-time recovery (PITR):

On-demand backups: Create full backups of tables at any time without impacting performance.

PITR: Restores data to any point in the past 35 days, enabling disaster recovery.

• Backups are encrypted and can be retained indefinitely.

Q: How can you optimize cost in DynamoDB?

• Use on-demand capacity mode for unpredictable workloads.
• Choose provisioned throughput mode with auto-scaling for predictable workloads.
• Use DynamoDB tables with infrequent access (DynamoDB Standard-IA) for cost savings on less-frequently accessed data.
• Implement efficient indexes: Use only necessary GSIs/LSIs to reduce storage and query costs.
• Leverage batch operations to minimize read and write costs.

Q: How does DynamoDB integrate with other AWS services?

Answer: DynamoDB integrates seamlessly with various AWS services, such as:

• AWS Lambda: For event-driven workflows and processing DynamoDB streams.
• Amazon S3: For storing large objects referenced in DynamoDB.
• AWS Glue: For data transformations and ETL processes.
• Amazon CloudWatch: For monitoring and setting alarms for key performance metrics.
• Amazon Kinesis: For real-time data streaming and analytics.

Q: What is the difference between DynamoDB and traditional relational databases?

Q: How does DynamoDB implement fine-grained access control?

• DynamoDB integrates with AWS Identity and Access Management (IAM) to provide fine-grained access control.
• Features include:
• Role-based access: Restrict access based on user roles.
• Attribute-based access: Limit access to specific attributes within an item.
• Condition-based policies: Apply conditions to grant access, such as IP address, time of day, or operation type.

Q: What is the maximum throughput capacity of a single DynamoDB partition?

• A single DynamoDB partition can support:
• 3,000 RCUs (read capacity units) for strongly consistent reads.
• 6,000 RCUs for eventually consistent reads.
• 1,000 WCUs (write capacity units).
• If workload exceeds these limits, DynamoDB automatically creates additional partitions to handle the demand.

Q: What are time-to-live (TTL) attributes in DynamoDB?

• TTL is a mechanism that automatically deletes expired items based on a specified timestamp.

Benefits:

• Reduces storage costs by cleaning up old or irrelevant data.
• Improves application performance by keeping the dataset smaller.
• Common use cases include session management, event logging, and temporary data storage.

Q: How does DynamoDB support global tables?

• Global tables enable multi-region, multi-active replication.

Features include:

• Automatic replication of table updates across multiple regions.
• Low-latency access for globally distributed applications.
• Built-in conflict resolution for write operations across regions.
• Common use cases include disaster recovery, geographically distributed workloads, and compliance with data residency requirements.

Q: How can you avoid hot partitions in DynamoDB?

• Hot partitions occur when too much traffic is directed to a single partition. To avoid them:
• Choose a partition key that evenly distributes traffic.
• Use randomized partition keys (e.g., hashing or adding random suffixes).
• Leverage composite keys to spread data across partitions.
• Avoid sequential keys such as timestamps for high-traffic workloads.

Q: What are some common DynamoDB error codes and their meanings?

• ProvisionedThroughputExceededException: The request exceeds the provisioned throughput. Solution: Increase RCUs/WCUs or optimize queries.
• ConditionalCheckFailedException: A conditional operation failed. Solution: Check and revise conditions.
• ThrottlingException: Too many requests are sent in a short period. Solution: Use exponential backoff or optimize capacity.
• ResourceNotFoundException: The specified table or index does not exist. Solution: Verify the resource name and ensure it exists.

Q: What is Adaptive Capacity in DynamoDB?

• Adaptive Capacity automatically adjusts partition throughput to accommodate uneven workloads.

Benefits:

• Prevents throttling on heavily accessed partitions.
• Ensures even distribution of capacity across partitions.
• Requires no manual intervention or reconfiguration.

Q: How does DynamoDB handle batch operations?

• DynamoDB supports two types of batch operations:
• BatchGetItem: Retrieves up to 100 items from multiple tables in a single request.
• BatchWriteItem: Writes, deletes, or updates up to 25 items in a single request.

Benefits:

• Reduces network overhead.
• Improves performance for bulk data processing.
• Note: Batch operations are subject to size limits and can return unprocessed items if capacity is exceeded.

Q: What are DynamoDB reserved capacity and savings plans?

• Reserved capacity allows you to commit to a specific capacity for a 1- or 3-year term at a discounted price.
• Savings plans provide flexible pricing based on usage patterns while reducing costs.
• Suitable for applications with predictable and consistent workloads.

Q: How does DynamoDB integrate with machine learning?

• DynamoDB integrates with Amazon SageMaker and AWS Glue to facilitate ML workflows.

Use cases:

• Store training data in DynamoDB and process it for model training.
• Use DynamoDB streams to trigger real-time ML predictions or insights.
• Implement feature stores with DynamoDB for serving real-time features to ML models.

Q: How does DynamoDB differ from Amazon RDS?

Q: What are transactional APIs in DynamoDB?

• Transactional APIs support ACID (Atomicity, Consistency, Isolation, Durability) operations.

APIs include:

• TransactWriteItems: Perform up to 25 write operations in a single transaction.
• TransactGetItems: Retrieve up to 25 items from one or more tables.

Use cases:

• Financial systems, inventory management, and multi-step workflows requiring strong data consistency.

Q: How does DynamoDB manage item versioning with optimistic locking?

• DynamoDB supports optimistic locking using a version number attribute.

Workflow:

• A version number is incremented every time an item is updated.
• Updates fail if the version number does not match the latest version in the database.
• Ensures data consistency and prevents accidental overwrites in concurrent environments.