What is the primary purpose of suppressing intermediate results in Kafka Streams?
Apache Kafka - Real-time Stream Processing (Master Class) - Supressing Intermediate Results

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
To improve processing speed
To enhance data security
To reduce memory usage
To prevent false alerts in threshold monitoring
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of heartbeat monitoring, what is the significance of a grace period?
It reduces the number of heartbeats required
It decreases the alert threshold
It allows for late-arriving heartbeats to be considered
It increases the processing time
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main issue with continuous updates in Kafka Streams for threshold monitoring?
They cause delays in processing
They lead to premature alerts
They increase memory usage
They complicate the code
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does the suppress method help in handling continuous updates?
By discarding old data
By buffering intermediate results until the window closes
By increasing the processing speed
By reducing the number of events processed
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What happens when the window is fully closed in Kafka Streams?
The system resets the count to zero
The final count is computed and alerts are sent if necessary
All intermediate results are discarded
The grace period is extended
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to have an unbounded buffer when using the suppress method?
To ensure all intermediate results are buffered until the window closes
To store all incoming data indefinitely
To reduce the memory usage
To increase the speed of processing
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the outcome when a late-arriving heartbeat is within the grace period?
It triggers an immediate alert
It is ignored
It is processed and included in the count
It resets the window
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