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NEW QUESTION # 84
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 is CPU-bound and lagging, adding more consumers to the group will allow better parallel processing, especially since the topic has 4 partitions, allowing up to 4 active consumers.
From Kafka 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 # 85
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?
Answer: C
Explanation:
To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.
From Kafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record." A does not prevent re-processing the bad record.
C is invalid; there's no skip() method in the Kafka consumer API.
D results in service interruption - not ideal for resiliency.
Reference: Kafka Consumer API - Exception Handling and seek()
NEW QUESTION # 86
StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> textLines = builder.stream("word-count-input"); KTable<String, Long> wordCounts = textLines
.mapValues(textLine -> textLine.toLowerCase())
.flatMapValues(textLine -> Arrays.asList(textLine.split("\W+")))
.selectKey((key, word) -> word)
.groupByKey()
.count(Materialized.as("Counts"));
wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long())); builder.build(); What is an adequate topic configuration for the topic word-count-output?
Answer: A
Explanation:
Result is aggregated into a table with key as the unique word and value its frequency. We have to enable log compaction for this topic to align the topic's cleanup policy with KTable semantics.
NEW QUESTION # 87
To get acknowledgement of writes to only the leader partition, we need to use the config...
Answer: C
Explanation:
Producers can set acks=1 to get acknowledgement from partition leader only.
NEW QUESTION # 88
(A consumer application runs once every two weeks and reads from a Kafka topic.
The last time the application ran, the last offset processed was 217.
The application is configured with auto.offset.reset=latest.
The current offsets in the topic start at 318 and end at 588.
Which offset will the application start reading from when it starts up for its next run?)
Answer: B
Explanation:
According to the official Apache Kafka consumer documentation, the auto.offset.reset configuration is only applied when a consumer has no valid committed offset for a partition. In this scenario, the consumer last committed offset 217, but the topic's earliest available offset is now 318. This means the previously committed offset is out of range, most likely due to topic retention cleanup.
When a committed offset is out of range, Kafka treats it as invalid and applies the auto.offset.reset policy.
Since the consumer is configured with auto.offset.reset=latest, it will start reading from the end of the log, not from the earliest available offset.
Kafka defines the "latest" offset as the log end offset, which is the offset of the next message to be written.
Given that the topic's offsets end at 588, the next offset is 589.
Therefore, the consumer will start reading from offset 589, skipping all existing records in the topic, which aligns with the documented Kafka consumer behavior.
NEW QUESTION # 89
......
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