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| Section | Objectives |
|---|---|
| Topic 1: Kafka Fundamentals | - Kafka architecture and core concepts
|
| Topic 2: Kafka Producers and Consumers | - Producer API
|
| Topic 3: Kafka Streams and Processing | - Kafka Streams API
|
| Topic 4: Kafka Ecosystem and Confluent Platform | - ksqlDB and connectors
|
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NEW QUESTION # 56
A stream processing application is tracking user activity in online shopping carts.
You want to identify periods of user inactivity.
Which type of Kafka Streams window should you use?
Answer: B
Explanation:
Session windows are ideal for tracking periods of activity separated by inactivity, such as user sessions.
From Kafka Streams Documentation > Windowing:
"A session window captures streams of events that are intermittent and separated by a gap of inactivity." Tumbling/Hopping/Sliding windows are fixed in size Session windows are dynamic and close after inactivity timeout This makes them perfect for identifying gaps in user interaction.
Reference: Kafka Streams Developer Guide > Session Windows
NEW QUESTION # 57
Which two producer exceptions are examples of the class RetriableException? (Select two.)
Answer: A,B
Explanation:
RetriableException is a subclass of KafkaException, indicating transient issues that might succeed if retried.
Examples include:
LeaderNotAvailableException: Happens when metadata is not yet propagated or leader election is in progress.
NotEnoughReplicasException: Happens when the number of in-sync replicas is insufficient. This can be transient if replicas come back online.
From the Apache Kafka Java Client documentation:
"These exceptions (like LeaderNotAvailableException, NotEnoughReplicasException) are transient and the client will retry them automatically depending on retry configuration." RecordTooLargeException and AuthorizationException are non-retriable as they indicate client-side or permission errors.
Reference: Apache Kafka Java Client API > org.apache.kafka.common.errors
NEW QUESTION # 58
You have a topic with four partitions. The application reads from it using two consumers in a single consumer group.
Processing is CPU-bound, and lag is increasing.
What should you do?
Answer: B
Explanation:
If the application isCPU-boundandlagging, addingmore consumersto the group will allow betterparallel processing, especially since the topic has4 partitions, allowing up to 4 active consumers.
FromKafka Consumer Group Docs:
"Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized."
* B may help but requires repartitioning and coordination.
* C or D affects how much data is polled, not how fast it's processed.
Reference:Kafka Consumer Concepts > Parallelism and Scaling
NEW QUESTION # 59
Suppose you have 6 brokers and you decide to create a topic with 10 partitions and a replication factor of 3. The brokers 0 and 1 are on rack A, the brokers 2 and 3 are on rack B, and the brokers 4 and 5 are on rack C.
If the leader for partition 0 is on broker 4, and the first replica is on broker 2, which broker can host the last replica? (select two)
Answer: A,D
Explanation:
When you create a new topic, partitions replicas are spreads across racks to maintain availability. Hence, the Rack A, which currently does not hold the topic partition, will be selected for the last replica
NEW QUESTION # 60
When using the Confluent Kafka Distribution, where does the schema registry reside?
Answer: C
Explanation:
Schema registry is a separate application that provides RESTful interface for storing and retrieving Avro schemas.
NEW QUESTION # 61
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