__________ is a mechanism provided by DynamoDB Streams to ensure that each shards data is processed in the correct order.
- Partition keys
- Sequence numbers
- Shard iterators
- Stream records
Sequence numbers in DynamoDB Streams ensure that records within a shard are processed in the correct order, maintaining data consistency.
DynamoDB Streams provide an at-least-once __________ of stream records, ensuring durability and data consistency.
- Delivery
- Execution
- Processing
- Retransmission
DynamoDB Streams ensure at-least-once delivery of stream records, meaning each record is delivered to the consumer at least once, ensuring durability and data consistency.
Scenario: You are designing an application where you need to perform real-time analytics on data changes in a DynamoDB table. How would you implement this using DynamoDB Streams and AWS Lambda?
- Create a Lambda function triggered by DynamoDB Streams
- Directly query the DynamoDB table for changes
- Schedule periodic batch jobs with Lambda
- Use AWS Glue for ETL jobs
Creating a Lambda function triggered by DynamoDB Streams allows you to process changes in real time, enabling real-time analytics.
Scenario: Your team is building a system where data integrity is crucial, and you're considering using DynamoDB Streams for change tracking. What are some considerations you need to keep in mind regarding data consistency and reliability?
- Ensure idempotency in Lambda functions
- Ignore duplicate records
- Rely on DynamoDB's default retry behavior
- Use eventual consistency for all operations
Ensuring idempotency in Lambda functions is crucial to maintain data integrity and reliability when using DynamoDB Streams for change tracking.
AWS Lambda@Edge enables you to run Lambda functions at __________ locations for improved latency.
- Cloud
- Data center
- Edge
- Regional
AWS Lambda@Edge enables you to run Lambda functions at edge locations for improved latency, allowing you to process data closer to the end user.
Scenario: You are designing a serverless architecture for a real-time analytics application. Which AWS service would you use to process incoming data streams and trigger AWS Lambda functions?
- Amazon Kinesis
- Amazon S3
- Amazon Redshift
- Amazon EC2
Amazon Kinesis is the correct option as it is designed for real-time processing of streaming data and can trigger AWS Lambda functions based on data events.
Scenario: Your team is concerned about optimizing the performance of AWS Lambda functions. What strategies would you recommend to minimize cold start times?
- Adding more logging statements
- Implementing provisioned concurrency
- Increasing memory allocation
- Reducing code size
Implementing provisioned concurrency is the recommended strategy to minimize cold start times in AWS Lambda functions as it allocates a set of concurrent executions to keep instances warm.
Scenario: You need to create a serverless API that triggers AWS Lambda functions based on HTTP requests. Which AWS service would you use to manage the API endpoints and integrate with AWS Lambda?
- Amazon API Gateway
- AWS Lambda itself
- Amazon SQS
- AWS Step Functions
Amazon API Gateway is the correct option as it is a fully managed service designed for managing API endpoints and integrating with AWS Lambda for triggering functions based on HTTP requests.
What are custom event sources in AWS Lambda?
- Built-in AWS services
- External services or applications
- Lambda-managed sources
- On-premises data centers
Custom event sources in AWS Lambda refer to external services or applications that can trigger the execution of Lambda functions by emitting custom events.
How do custom event sources differ from standard event sources in AWS Lambda?
- Customizability
- Built-in compatibility
- Scalability
- Performance
Custom event sources offer more flexibility and customization options compared to standard event sources, allowing developers to define their own event formats and sources.