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NEW QUESTION # 65
Which is true about topic compaction?
Answer: A
Explanation:
Log compactionensures that Kafka retains at least thelatest value per keyin a topic. Compaction happensin the backgroundand removes older records with the same keyin inactive log segments, not immediately.
From theKafka Documentation > Log Compaction:
"Kafka guarantees that thelast message for each keywill be retained in the log after compaction, even if earlier messages with the same key are deleted." So, D is correct. A is incorrect because compaction does not overwrite; it's a background process. B is incorrect-deletion is not immediate. C incorrectly suggests client-side hashmap behavior.
Reference:Apache Kafka Log Compaction Docs
NEW QUESTION # 66
You create a producer that writes messages about bank account transactions from tens of thousands of different customers into a topic.
* Your consumers must process these messages with low latency and minimize consumer lag
* Processing takes ~6x longer than producing
* Transactions for each bank account must be processedin orderWhich strategy should you use?
Answer: A
Explanation:
To maintainmessage ordering per bank account, themessage key must be the account number. Kafka guaranteesordering per partition, and the partition is selected based on thehash of the key.
FromKafka Producer Docs:
"All records with the same key will be sent to the same partition, and Kafka preserves the order of records in a partition." Using account number ensures:
* All messages for the same account go to the same partition
* Order is maintained
* Workload is evenly distributed if account keys are varied
Other options break ordering or introduce unnecessary randomness.
Reference:Kafka Producer Concepts > Partitions and Keys
NEW QUESTION # 67
In Kafka, what are Topics split into?
Answer: A
NEW QUESTION # 68
You need to correctly join data from two Kafka topics.
What will allow for the co-partitioning? (Choose 2.)
Answer: C,D
NEW QUESTION # 69
(You have a topic with four partitions. The application reading this topic is using a consumer group with two consumers.
Throughput is smoothly distributed among partitions, but application lag is increasing.
Application monitoring shows that message processing is consuming all available CPU resources.
Which action should you take to resolve this issue?)
Answer: A
Explanation:
According to the Apache Kafka consumer and scalability documentation, parallelism in message processing is primarily determined by the number of partitions and the number of consumers within a consumer group.
Each partition can be assigned to only one consumer at a time. In this scenario, the topic already has four partitions, but only two consumers are processing them, meaning only two partitions are actively consumed in parallel.
Since monitoring indicates that message processing is CPU-bound, the bottleneck is not Kafka I/O but application-side processing. Adding more consumers (up to the number of partitions) allows Kafka to rebalance the partitions and distribute them across more consumer instances, increasing CPU parallelism and reducing lag.
Option A (adding more partitions) can increase parallelism but is more intrusive and may require repartitioning considerations for keys and downstream systems. Option B increases batch size, which can worsen CPU pressure. Option D may reduce per-poll workload but does not increase parallel processing capacity.
Therefore, the correct and recommended action per Kafka documentation is to add more consumers to the consumer group.
NEW QUESTION # 70
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