Skip to content

Latest commit

 

History

History
159 lines (100 loc) · 6.16 KB

Questions1.md

File metadata and controls

159 lines (100 loc) · 6.16 KB

Question 1

Which tool is commonly used to monitor Kafka cluster health and performance?

  • A. Nagios
  • B. Prometheus
  • C. Elasticsearch
  • D. Splunk

Explanation: Prometheus is widely used for monitoring Kafka cluster health and performance. It collects metrics from Kafka brokers, producers, and consumers, and stores them in a time-series database. Prometheus can be used with Grafana for visualizing these metrics.

  • A, C, and D are incorrect because while Nagios, Elasticsearch, and Splunk can be used for monitoring and logging, Prometheus is more specialized for metrics collection and monitoring.

Answer: B

Question 2

What is the primary purpose of JMX in the context of Kafka monitoring?

  • A. To configure Kafka brokers
  • B. To provide real-time logging of Kafka events
  • C. To expose Kafka metrics for monitoring
  • D. To manage Kafka ACLs

Explanation: JMX (Java Management Extensions) is used to expose Kafka metrics for monitoring. Kafka brokers expose various metrics (such as broker metrics, topic metrics, and consumer group metrics) through JMX, which can be collected and monitored by tools like Prometheus.

  • A is incorrect because JMX is not used for configuring Kafka brokers. B is incorrect because JMX is not primarily used for real-time logging. D is incorrect because JMX does not manage Kafka ACLs.

Answer: C

Question 3

Which Kafka metric would you monitor to detect message delivery delays in a Kafka cluster?

  • A. MessagesInPerSec
  • B. RequestLatencyMs
  • C. UnderReplicatedPartitions
  • D. BytesOutPerSec

Explanation: RequestLatencyMs is the Kafka metric that indicates the latency of requests. Monitoring this metric can help detect message delivery delays in a Kafka cluster.

  • A, C, and D are incorrect because they measure different aspects: MessagesInPerSec measures the rate of messages being produced, UnderReplicatedPartitions indicates partition replication issues, and BytesOutPerSec measures the rate of bytes being consumed.

Answer: B

Question 4

Which of the following tools can be used to visualize Kafka metrics collected by Prometheus?

  • A. Kibana
  • B. Grafana
  • C. Logstash
  • D. Fluentd

Explanation: Grafana is a popular tool used to visualize metrics collected by Prometheus. It can create dashboards to monitor Kafka metrics and provide insights into cluster performance.

  • A, C, and D are incorrect because while Kibana is used for visualizing data from Elasticsearch, Logstash and Fluentd are used for log processing, not for visualizing Prometheus metrics.

Answer: B

Question 5

What does the Kafka metric UnderReplicatedPartitions indicate?

  • A. The number of partitions without a leader
  • B. The number of partitions that have fewer replicas than specified
  • C. The number of partitions that are not receiving messages
  • D. The number of partitions with high message latency

Explanation: The UnderReplicatedPartitions metric indicates the number of partitions that have fewer replicas than specified in their replication factor. This metric helps identify potential data reliability issues in the Kafka cluster.

  • A, C, and D are incorrect because they describe different aspects of Kafka partition health and performance.

Answer: B

Question 6

Which Kafka metric should be monitored to ensure sufficient disk space on Kafka brokers?

  • A. LogEndOffset
  • B. LogSegmentCount
  • C. FreeStorageSpace
  • D. MessageRate

Explanation: The FreeStorageSpace metric should be monitored to ensure that Kafka brokers have sufficient disk space. This metric helps prevent disk-related issues that can affect Kafka performance and stability.

  • A, B, and D are incorrect because they measure different aspects of Kafka performance and health, not disk space availability.

Answer: C

Question 7

What is the role of Kafka Exporter in a Kafka monitoring setup?

  • A. To collect logs from Kafka brokers
  • B. To expose Kafka metrics to Prometheus
  • C. To configure Kafka broker settings
  • D. To manage Kafka consumer groups

Explanation: Kafka Exporter is used to expose Kafka metrics to Prometheus. It collects metrics from Kafka brokers and provides them in a format that Prometheus can scrape and store for monitoring purposes.

  • A, C, and D are incorrect because Kafka Exporter does not collect logs, configure broker settings, or manage consumer groups.

Answer: B

Question 8

Which Kafka metric indicates the time it takes for a record to be acknowledged by all in-sync replicas?

  • A. ReplicationLag
  • B. FetchLatency
  • C. ProducerLatency
  • D. ISRTime

Explanation: ReplicationLag indicates the time it takes for a record to be acknowledged by all in-sync replicas. Monitoring this metric helps ensure data consistency and reliability within the Kafka cluster.

  • B, C, and D are incorrect because they describe different latency measurements related to fetching, producing, and in-sync replica time.

Answer: A

Question 9

Why is it important to monitor the RequestRate metric in Kafka?

  • A. To measure the number of messages being produced
  • B. To measure the number of bytes being consumed
  • C. To measure the rate of requests being handled by Kafka brokers
  • D. To measure the number of partitions

Explanation: Monitoring the RequestRate metric is important because it measures the rate of requests being handled by Kafka brokers. High request rates can indicate high load on the brokers, which may affect their performance.

  • A, B, and D are incorrect because they describe different aspects of Kafka performance and health.

Answer: C

Question 10

What does the Kafka metric ConsumerLag indicate?

  • A. The number of messages a consumer has consumed
  • B. The number of messages a consumer is behind in processing
  • C. The time a consumer takes to process a message
  • D. The number of partitions a consumer is subscribed to

Explanation: The ConsumerLag metric indicates the number of messages a consumer is behind in processing. It is a crucial metric for ensuring that consumers are keeping up with the message production rate and not falling behind.

  • A, C, and D are incorrect because they describe different aspects of consumer performance and subscriptions.

Answer: B