Scenario: You are building a real-time notification system for a mobile app. Which AWS service would you use to send push notifications and trigger AWS Lambda functions to process them?
- AWS IoT
- Amazon SES
- Amazon SNS
- Amazon SQS
Amazon SNS can send push notifications to mobile devices and trigger AWS Lambda functions for processing them.
Scenario: Your application receives a sudden surge of incoming messages through SNS. How can you ensure that the AWS Lambda functions triggered by these messages can handle the increased load efficiently?
- Enable concurrency limits
- Increase Lambda memory allocation
- Use AWS Batch
- Use Step Functions
Setting concurrency limits helps manage the number of concurrent executions, preventing Lambda functions from being overwhelmed.
Scenario: You need to implement a serverless architecture where incoming data from IoT devices triggers AWS Lambda functions for processing. How would you design the integration between SNS and AWS Lambda in this scenario?
- Deploy EC2 instances
- Publish data to SNS topic
- Use S3 for data storage
- Utilize Kinesis Data Streams
Publishing data to an SNS topic allows SNS to trigger AWS Lambda functions for processing the incoming data from IoT devices.
What is the purpose of a DynamoDB stream ARN (Amazon Resource Name)?
- Creating backup snapshots
- Granting IAM permissions
- Identifying a specific stream
- Monitoring stream activity
A DynamoDB stream ARN uniquely identifies a specific stream.
How can you ensure ordered processing of records in DynamoDB Streams?
- Enable cross-region replication
- Implement conditional writes
- Increase read capacity units
- Use partition keys
Using partition keys ensures that records with the same partition key are processed in order.
What are some use cases for integrating DynamoDB Streams with AWS Lambda?
- Load balancing
- Long-term data storage
- Real-time analytics
- Static website hosting
Real-time analytics, such as processing and analyzing data changes in real-time, is a key use case for integrating DynamoDB Streams with AWS Lambda.
How does DynamoDB Streams handle data consistency across multiple shards?
- Batch processing
- Parallel processing
- Sequence numbers
- Timestamps
DynamoDB Streams uses sequence numbers to maintain the correct order of records, ensuring data consistency across multiple shards.
What are the limitations of DynamoDB Streams regarding scalability and performance?
- High latency
- Lack of data encryption
- Limited read throughput
- Limited write capacity
The limited read throughput of DynamoDB Streams can impact scalability and performance, particularly when processing high volumes of data.
DynamoDB Streams are triggered by changes to __________ tables.
- DynamoDB
- RDS
- Redshift
- S3
DynamoDB Streams are triggered by changes to DynamoDB tables, capturing data modifications and enabling subsequent processing.
__________ is the process of capturing a time-ordered sequence of item-level modifications in a DynamoDB table.
- Change data capture
- Data replication
- Data warehousing
- ETL
Change data capture is the process of capturing a time-ordered sequence of item-level modifications in a DynamoDB table.