A company stores sales transaction data in Amazon DynamoDB tables. To detect anomalous behaviors and respond quickly, all changes to the items stored in the DynamoDB tables must be logged within 30 minutes. Which solution meets the requirements?
A.
Copy the DynamoDB tables into Apache Hive tables on Amazon EMR every hour and analyze them for anomalous behaviors. Send Amazon SNS notifications when anomalous behaviors are detected.
B.
Use AWS CloudTrail to capture all the APIs that change the DynamoDB tables. Send SNS notifications when anomalous behaviors are detected using CloudTrail event filtering.
C.
Use Amazon DynamoDB Streams to capture and send updates to AWS Lambda. Create a Lambda function to output records to Amazon Kinesis Data Streams. Analyze any anomalies with Amazon Kinesis Data Analytics. Send SNS notifications when anomalous behaviors are detected.
D.
Use event patterns in Amazon CloudWatch Events to capture DynamoDB API call events with an AWS Lambda function as a target to analyze behavior. Send SNS notifications when anomalous behaviors are detected.
C is correct.
https://aws.amazon.com/blogs/database/dynamodb-streams-use-cases-and-design-patterns/#:~:text=DynamoDB%20Streams%20is%20a%20powerful,for%20up%20to%2024%20hours.
You can use Amazon Kinesis Data Streams to capture changes to Amazon DynamoDB.
Kinesis Data Streams captures item-level modifications in any DynamoDB table and replicates them to a Kinesis data stream. Your applications can access this stream and view item-level changes in near-real time.
Does anyone review/moderate?? Almost 100% votes are for C. C is the correct answer as dynamoDB streams will provide original and modified values which the lambda can forward to kinesis for event processing. Cloudwatch events is not going to do what is expected
Miss spelling:
C is OK, but I wonder that is it too fast with the questione only require within 30 minutes, while with DynamoDB Streams we have a real-time...
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