Free Top Amazon/AWS DEA-C01 Actual Exam Questions - Question 11 Discussion
thousand data points each second. The company runs an application to process the usage data in real
time. The company aggregates and stores the data in an Amazon Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be able to
identify sudden drops in network usage so the company can take immediate remedial actions.
Which solution will meet this requirement with the LEAST latency?
It’s B because streaming data through Kinesis with Apache Flink lets you detect drops instantly without waiting for batch queries. That’s way faster than polling every minute like A or C.
C imo, querying DynamoDB with DAX should be faster than Aurora queries every minute.
It’s B. Using Kinesis with Apache Flink is made for real-time streaming analytics, so it can spot sudden drops right when they happen. Unlike Lambda functions that poll every minute (which adds delay), this method processes data continuously as it flows in. Even if the app needs some tweaking to send data to Kinesis, this setup offers the lowest latency detection by design.
D - Aurora streams avoid polling delays, so it’s the fastest way to catch drops.
Option D detects usage drops immediately via Aurora streams, avoiding polling delays.
D/C? D uses Aurora’s built-in streams, so it reacts instantly without polling. C still polls every minute, so higher latency despite DAX speeding up reads.
Maybe D could work too since Database Activity Streams can capture changes instantly. Using a Lambda triggered by those streams might detect drops with very low latency without polling every minute.
This one seems to be about low latency real-time detection. Option B looks best since Kinesis with Apache Flink is designed for streaming and real-time processing, so it should catch drops quickly. The others involve querying databases periodically or after events, which adds delay. So I’d say B.