MCQs on Kafka Monitoring and Management | Apache Kafka MCQs Questions
Welcome to a comprehensive set of Apache Kafka MCQs Questions focused on Kafka monitoring and management. These MCQs cover essential aspects such as monitoring Kafka clusters with metrics, using tools like Prometheus, Grafana, and JMX, log aggregation and analysis, handling cluster failures, and optimizing Kafka performance. Understanding these concepts is crucial for managing Kafka efficiently in production environments. Whether you’re preparing for exams or improving your practical knowledge, these questions will guide you in mastering Kafka’s monitoring and management capabilities for optimized performance and failure handling.
Chapter 8: Kafka Monitoring and Management – MCQs
Topic 1: Monitoring Kafka Clusters with Metrics
- Which Kafka metric represents the number of messages successfully consumed?
a) consumer_lag
b) messages_consumed
c) consumer_rate
d) total_consumed - What is the metric used to monitor the number of bytes read by Kafka consumers?
a) consumer_bytes_read
b) fetch_rate
c) bytes_consumed
d) fetch_bytes - Which tool can be used to monitor Kafka’s JVM memory usage?
a) Prometheus
b) Grafana
c) JMX
d) Zookeeper - Which of the following is an important metric for Kafka brokers to track the status of replication?
a) leader_election
b) replication_lag
c) replication_factor
d) broker_uptime - How can Kafka brokers ensure message delivery with low latency?
a) High replication factor
b) Monitoring consumer lag
c) Configuring partition reassignment
d) Using larger message sizes
Topic 2: Tools for Kafka Monitoring: Prometheus, Grafana, and JMX
- What is the main purpose of Prometheus in Kafka monitoring?
a) Data encryption
b) Real-time metrics collection
c) Message production
d) Partition balancing - Which tool is commonly used to visualize Kafka metrics collected by Prometheus?
a) Grafana
b) Kibana
c) Jupyter
d) Tableau - Which Java tool can be integrated with Kafka to expose metrics for monitoring?
a) JMX
b) Kafka Manager
c) Grafana
d) Prometheus - What is the benefit of using JMX with Kafka?
a) It allows real-time data replication
b) It enables remote monitoring of Kafka’s JVM metrics
c) It automates Kafka configuration
d) It encrypts messages in transit - Which of the following is a Kafka metric that Prometheus can track?
a) Producer replication status
b) Kafka topic partition offset
c) Consumer group lag
d) Partition leader change time
Topic 3: Log Aggregation and Analysis
- What is the primary purpose of log aggregation in Kafka?
a) To collect log data from Kafka consumers
b) To store logs for future retrieval
c) To consolidate logs from multiple brokers and applications
d) To increase Kafka’s processing speed - Which of the following tools is commonly used for aggregating logs in Kafka?
a) Fluentd
b) Zookeeper
c) Prometheus
d) Kafka Streams - Which format is typically used to store Kafka logs for analysis?
a) CSV
b) JSON
c) Avro
d) Parquet - What is one key advantage of centralized log aggregation for Kafka?
a) Faster topic creation
b) Simplified log analysis and troubleshooting
c) Improved message compression
d) Increased replication factor - What is the main use of log analysis in Kafka clusters?
a) To monitor the health of topics
b) To ensure correct data distribution
c) To identify and resolve issues quickly
d) To configure Kafka security
Topic 4: Handling Cluster Failures and Recovery
- What is the first step in handling a Kafka cluster failure?
a) Reconfigure consumer groups
b) Check broker logs
c) Restart the cluster
d) Rebalance partitions - What Kafka tool helps in recovering from broker failures by electing a new leader for partitions?
a) Kafka Streams
b) Kafka Controller
c) Kafka Connect
d) Zookeeper - Which Kafka feature ensures data replication in case of broker failure?
a) Consumer groups
b) Partition replication
c) Topic retention
d) Compression - In case of a failed Kafka broker, what should be the first priority for recovery?
a) Restoring consumer offsets
b) Increasing replication factor
c) Rebalancing partitions
d) Restoring message retention policies - What is a key strategy to handle Kafka cluster failure?
a) Frequent backups
b) Single-node setup
c) Disable topic retention
d) Increasing topic partition count
Topic 5: Optimizing Kafka Performance
- Which setting can help improve Kafka’s performance by increasing data throughput?
a) Increasing replication factor
b) Increasing batch size
c) Decreasing consumer lag
d) Reducing partition count - How can Kafka optimize its message delivery latency?
a) By increasing replication factor
b) By configuring high buffer sizes
c) By reducing the number of partitions
d) By optimizing partition distribution - What is the recommended practice for optimizing Kafka consumer throughput?
a) Increasing the consumer timeout
b) Using consumer groups with multiple consumers
c) Reducing the message batch size
d) Increasing the number of producers - What is the effect of setting a high replication factor in Kafka?
a) Increases message latency
b) Improves fault tolerance at the cost of storage
c) Reduces the number of partitions
d) Decreases resource consumption - Which of the following is a performance optimization feature available in Kafka?
a) Automatic topic partition assignment
b) Disk-based memory management
c) Producer-side batching
d) Zero message compression - How does Kafka handle large data throughput with minimal performance degradation?
a) By using real-time replication
b) By leveraging partitioning and parallelism
c) By limiting broker connections
d) By increasing the number of consumer groups - What is the effect of a poorly configured replication factor on Kafka performance?
a) Faster message delivery
b) Increased storage costs and risk of data loss
c) Better fault tolerance
d) Improved network latency - Which factor is crucial for improving Kafka’s write performance?
a) High memory capacity
b) Balanced partition distribution
c) Shorter message retention periods
d) Increased consumer processing speed - What does “compression” in Kafka help with in terms of performance?
a) It decreases message latency
b) It reduces the amount of storage required
c) It increases the number of partitions
d) It improves message consistency - What is one way to optimize Kafka cluster resource usage?
a) Limiting the number of partitions
b) Reducing broker replication
c) Using only one consumer
d) Monitoring consumer lag
Answer Key
| Qno | Answer |
|---|---|
| 1 | c) consumer_rate |
| 2 | d) fetch_bytes |
| 3 | c) JMX |
| 4 | b) replication_lag |
| 5 | b) Monitoring consumer lag |
| 6 | b) Real-time metrics collection |
| 7 | a) Grafana |
| 8 | a) JMX |
| 9 | b) It enables remote monitoring of Kafka’s JVM metrics |
| 10 | c) Consumer group lag |
| 11 | c) To consolidate logs from multiple brokers and applications |
| 12 | a) Fluentd |
| 13 | b) JSON |
| 14 | b) Simplified log analysis and troubleshooting |
| 15 | c) To identify and resolve issues quickly |
| 16 | b) Check broker logs |
| 17 | b) Kafka Controller |
| 18 | b) Partition replication |
| 19 | c) Rebalancing partitions |
| 20 | a) Frequent backups |
| 21 | b) Increasing batch size |
| 22 | b) By configuring high buffer sizes |
| 23 | b) Using consumer groups with multiple consumers |
| 24 | b) Improves fault tolerance at the cost of storage |
| 25 | c) Producer-side batching |
| 26 | b) By leveraging partitioning and parallelism |
| 27 | b) Increased storage costs and risk of data loss |
| 28 | b) Balanced partition distribution |
| 29 | b) It reduces the amount of storage required |
| 30 | d) Monitoring consumer lag |