Apache Kafka Certification Exam Prep Kit

CONTENTS

(Multiple-Choice Questions

with Detailed Explanations)

1. Kafka Fundamentals

Core concepts: Topics, Partitions, Offsets

Producers and Consumers

Brokers, Clusters, and Replication

Kafka Commit Log Architecture

ZooKeeper and its role in Kafka

2. Kafka Producers

Producer API fundamentals

Message serialization and deserialization

Producer configuration (e.g., acks, retries, batch settings)

Partitioning strategies and partition keys

Idempotent Producers and Transactional Producers

3. Kafka Consumers

Consumer API fundamentals

Consumer groups and rebalancing

Offset management: manual and automatic commits

Consumer configuration (e.g., poll intervals, session timeouts)

Strategies: earliest, latest, or specific offset reads

4. Kafka Streams

Introduction to Kafka Streams API

Stream processing concepts: KStream, KTable

Stateless and stateful processing

Aggregations, Joins, and Windows

Stream-to-Stream and Stream-to-Table operations

5. Schema Management with Confluent Schema Registry

Schema Registry basics

Avro, Protobuf, and JSON schemas

Schema versioning and compatibility

Working with schemas in producers and consumers

Handling schema evolution

6. Kafka Connect

Basics of Kafka Connect API

Source and Sink connectors

Standalone vs Distributed modes

Connector configuration and deployment

Custom connector development and management

7. Security in Kafka

Authentication mechanisms: SSL, SASL (PLAIN, SCRAM, GSSAPI)

Authorization: ACLs

Data encryption: in-transit and at-rest

Secure communication between producers/consumers and brokers

8. Monitoring and Troubleshooting

Monitoring Kafka metrics using tools like Confluent Control Center, JMX, or Prometheus

Debugging producer and consumer issues

Partition rebalancing and its impact

Identifying bottlenecks and optimizing performance

9. Kafka Transactions

Transactional guarantees in Kafka

Exactly-once semantics

Handling transactional producers and consumers

Use cases for transactions

10. Advanced Kafka Features

Kafka Streams: Interactive Queries

Log Compaction

Rebalancing strategies and cooperative rebalancing

Kafka Tiered Storage

Quotas for producers and consumers

11 . 30-Day Study Plan for Confluent Certified Developer for Apache Kafka Certification

Introduction:

Welcome to Apache Kafka Certification Exam Prep kit, your ultimate guide to excelling in the Apache Kafka certification exam. This book is thoughtfully designed to support your journey toward mastering the essential skills and knowledge required to build, manage, and optimize powerful Kafka-based applications.

As organizations increasingly rely on Apache Kafka for real-time data streaming and processing, becoming certified as a Kafka developer has become a significant advantage in today’s competitive job market. This credential demonstrates your expertise in designing, implementing, and maintaining Kafka systems, positioning you as a sought-after professional in the fast-evolving world of data engineering.

Apache Kafka Certification Exam Prep kit features 220 exam-style questions and detailed explanations that closely align with the actual exam. Using a focused Q&A format, this book explores core Kafka concepts, practical applications, and real-world scenarios to enhance your understanding and problem-solving skills.

Whether you're revising core concepts, gaining hands-on experience, or tackling exam-level challenges, this guide is tailored to give you the confidence and expertise needed to ace the certification and thrive as an Apache Kafka developer.

Question 1:

What is the primary reason for a Kafka consumer group to undergo a rebalance?

Options

When a new topic is added to the cluster: A rebalance occurs when a new topic is added to the cluster, requiring the consumer group to adjust its partition assignments.

When a consumer joins or leaves the group: A rebalance occurs when a consumer joins or leaves the group, requiring the group to reassign partitions among the remaining consumers.

When a broker fails or is shut down: A rebalance occurs when a broker fails or is shut down, requiring the consumer group to adjust its partition assignments to account for the changed broker topology.

When a consumer's subscription changes: A rebalance occurs when a consumer's subscription changes, requiring the group to reassign partitions based on the new subscription.

Answer

When a consumer joins or leaves the group

Explanation

AKafka consumer group undergoes a rebalance when a consumer joins or leaves the group. This is because the group needs to reassign partitions among the remaining consumers to ensure that all partitions are consumed and that no consumer is overloaded. Rebalancing ensures that the consumer group can continue to process messages efficiently and effectively.

Question 2:

What happens to the consumer's current offset when a Kafka consumer group undergoes a rebalance?

Options

The consumer's current offset is reset to the beginning of the partition: During a rebalance, the consumer's current offset is reset to the beginning of the partition, causing the consumer to re-consume all messages in the partition.

The consumer's current offset is lost, and the consumer must restart from the last committed offset: During a rebalance, the consumer's current offset is lost, and the consumer must restart from the last committed offset, potentially causing message duplication or loss.

The consumer's current offset is maintained, and the consumer can continue consuming from the same offset: During a rebalance, the consumer's current offset is maintained, and the consumer can continue consuming from the same offset, ensuring that there is no message duplication or loss.

The consumer's current offset is updated to reflect the new partition assignment: During a rebalance, the consumer's current offset is updated to reflect the new partition assignment, ensuring that the consumer starts consuming from the correct offset in the new partition.

Answer

The consumer's current offset is maintained, and the consumer can continue consuming from the same offset

Explanation

When a Kafka consumer group undergoes a rebalance, the consumer's current offset is maintained, and the consumer can continue consuming from the same offset. This ensures that there is no message duplication or loss, and the consumer can continue processing messages efficiently and effectively.

Question 3:

What is the purpose of the auto.commit.interval.ms configuration in a Kafka consumer?

Options

To specify the frequency at which the consumer commits its current offset: The auto.commit.interval.ms configuration determines how often the consumer automatically commits its current offset to the broker.

To configure the consumer's session timeout: The auto.commit.interval.ms configuration sets the consumer's session timeout, determining how long the consumer can be inactive before being considered dead.

To specify the maximum amount of time the consumer will wait for a response from the broker: The auto.commit.interval.ms configuration determines the maximum amount of time the consumer will wait for a response from the broker before timing out.

To enable or disable manual offset commits: The auto.commit.interval.ms configuration enables or disables manual offset commits, allowing the consumer to control when its offsets are committed.

Answer

To specify the frequency at which the consumer commits its current offset

Explanation

The auto.commit.interval .ms configuration determines how often the consumer automatically commits its current offset to the broker. This ensures that the consumer's progress is persisted and allows the consumer to resume from its last committed offset in case of a failure.

Question 4:

What happens when a Kafka consumer calls the commitSync() method to commit its current offset, but the commit fails due to a broker error?

Options

The consumer will retry the commit indefinitely until it is successful: The consumer will continue to retry the commit until it is successful, ensuring that the offset is eventually committed.

The consumer will throw a CommitFailedException and terminate: The consumer will throw a CommitFailedException and terminate, requiring manual intervention to restart the consumer.

The consumer will revert to the last successfully committed offset: The consumer will revert to the last successfully committed offset, ensuring that no messages are lost due to the failed commit.

The consumer will continue consuming messages, but the offset will not be committed: The consumer will continue consuming messages, but the offset will not be committed, potentially leading to message duplication or loss.

Answer

The consumer will retry the commit indefinitely until it is successful

Explanation

When a Kafka consumer calls the commitSync() method to commit its current offset, but the commit fails due to a broker error, the consumer will retry the commit indefinitely until it is successful. This ensures that the offset is eventually committed, and the consumer's progress is persisted.

Question 5:

What is the purpose of the auto.commit.interval.ms configuration in a Kafka consumer?

Options

To specify the frequency at which the consumer commits its current offset: The auto.commit.interval.ms configuration determines how often the consumer automatically commits its current offset to the broker.

To configure the consumer's session timeout: The auto.commit.interval.ms configuration sets the consumer's session timeout, determining how long the consumer can be inactive before being considered dead.

To specify the maximum amount of time the consumer will wait for a response from the broker: The auto.commit.interval.ms configuration determines the maximum amount of time the consumer will wait for a response from the broker before timing out.

To enable or disable manual offset commits: The auto.commit.interval.ms configuration enables or disables manual offset commits, allowing the consumer to control when its offsets are committed.

Answer

To specify the frequency at which the consumer commits its current offset

Explanation

The auto.commit.interval .ms configuration determines how often the consumer automatically commits its current offset to the broker. This ensures that the consumer's progress is persisted and allows the consumer to resume from its last committed offset in case of a failure.

Question 6:

What happens when a Kafka consumer calls the commitSync() method to commit its current offset, but the commit fails due to a broker error?

Options

The consumer will retry the commit indefinitely until it is successful: The consumer will continue to retry the commit until it is successful, ensuring that the offset is eventually committed.

The consumer will throw a CommitFailedException and terminate: The consumer will throw a CommitFailedException and terminate, requiring manual intervention to restart the consumer.

The consumer will revert to the last successfully committed offset: The consumer will revert to the last successfully committed offset, ensuring that no messages are lost due to the failed commit.

The consumer will continue consuming messages, but the offset will not be committed: The consumer will continue consuming messages, but the offset will not be committed, potentially leading to message duplication or loss.

Answer

The consumer will retry the commit indefinitely until it is successful

Explanation

When a Kafka consumer calls the commitSync() method to commit its current offset, but the commit fails due to a broker error, the consumer will retry the commit indefinitely until it is successful. This ensures that the offset is eventually committed, and the consumer's progress is persisted.

Question 7:

What is the effect of setting enable.auto.commit to true and auto.commit.interval.ms to a non-zero value in a Kafka consumer?

Options

The consumer will commit its current offset after every message consumption: The consumer will commit its current offset after every message consumption, ensuring that the consumer's progress is persisted immediately.

The consumer will commit its current offset at a fixed interval, regardless of message consumption: The consumer will commit its current offset at a fixed interval, regardless of message consumption, ensuring that the consumer's progress is persisted periodically.

The consumer will not commit its current offset, relying on manual offset commits: The consumer will not commit its current offset, relying on manual offset commits to persist its progress.

The consumer will commit its current offset only when the consumer is shut down: The consumer will commit its current offset only when the consumer is shut down, ensuring that the consumer's progress is persisted only at termination.

Answer

The consumer will commit its current offset at a fixed interval, regardless of message consumption

Explanation

When enable.auto.commit is set to true and auto.commit.interval.ms is set to a non-zero value, the Kafka consumer will automatically commit its current offset at a fixed interval, regardless of message consumption. This ensures that the consumer's progress is persisted periodically, allowing the consumer to resume from its last committed offset in case of a failure.

Question 8:

What happens when a Kafka consumer's auto.commit.interval.ms is set to a value that is less than the time it takes to consume a single message?

Options

The consumer will commit its current offset after every message consumption: The consumer will commit its current offset after every message consumption, ensuring that the consumer's progress is persisted immediately.

The consumer will commit its current offset at the specified interval, potentially leading to message duplication: The consumer will commit its current offset at the specified interval, potentially leading to message duplication if the consumer fails before the next commit interval.

The consumer will not commit its current offset, relying on manual offset commits: The consumer will not commit its current offset, relying on manual offset commits to persist its progress.

The consumer will throw an exception, indicating that the commit interval is too short: The consumer will throw an exception, indicating that the commit interval is too short and cannot be used.

Answer

The consumer will commit its current offset at the specified interval, potentially leading to message duplication

Explanation

When a Kafka consumer's auto.commit.interval.ms is set to a value that is less than the time it takes to consume a single message, the consumer will commit its current offset at the specified interval. However, this can potentially lead to message duplication if the consumer fails before the next commit interval, as the consumer may re-consume messages that were already processed.

Question 9:

What is the effect of setting max.partition.fetch.bytes to a low value in a Kafka consumer?

Options

The consumer will fetch more messages from the broker in each request: Setting max.partition.fetch.bytes to a low value will cause the consumer to fetch more messages from the broker in each request, potentially improving throughput.

The consumer will fetch fewer messages from the broker in each request: Setting max.partition.fetch.bytes to a low value will cause the consumer to fetch fewer messages from the broker in each request, potentially reducing memory usage.

The consumer will ignore messages that exceed the specified size limit: Setting max.partition.fetch.bytes to a low value will cause the consumer to ignore messages that exceed the specified size limit, potentially leading to message loss.

The consumer will throw an exception if the broker returns more data than the specified limit: Setting max.partition.fetch.bytes to a low value will cause the consumer to throw an exception if the broker returns more data than the specified limit, potentially leading to consumer failure.

Answer

The consumer will fetch fewer messages from the broker in each request

Explanation

When max.partition .fetch.bytes is set to a low value, the Kafka consumer will fetch fewer messages from the broker in each request. This is because the consumer is limited by the amount of data it can fetch in a single request, rather than the number of messages. By reducing the amount of data fetched, the consumer can reduce its memory usage and potentially improve performance.

Question 10:

What is the purpose of the client.id configuration in a Kafka consumer?

Options

To specify the consumer group ID: The client.id configuration specifies the consumer group ID, which determines the group that the consumer belongs to.

To specify the broker connection timeout: The client.id configuration specifies the broker connection timeout, determining how long the consumer will wait to establish a connection to the broker.

To identify the consumer application: The client.id configuration identifies the consumer application, allowing the broker to track and manage connections from different clients.

To enable or disable SSL/TLS encryption: The client.id configuration enables or disables SSL/TLS encryption, determining whether the consumer will use secure connections to the broker.

Answer

To identify the consumer application

Explanation

The client.id configuration identifies the consumer application, allowing the broker to track and manage connections from different clients. This is useful for monitoring and debugging purposes, as it allows administrators to distinguish between different consumer applications and track their activity.

Question 11:

What happens when a Kafka consumer's poll() method is called with a timeout of 100ms, but the broker takes 200ms to respond with new messages?

Options

The consumer will throw a timeout exception and disconnect from the broker: The consumer will throw a timeout exception and disconnect from the broker, requiring a reconnect to resume consuming messages.

The consumer will block indefinitely, waiting for the broker to respond: The consumer will block indefinitely, waiting for the broker to respond with new messages, potentially causing the consumer to become unresponsive.

The consumer will return an empty ConsumerRecords collection and continue polling: The consumer will return an empty ConsumerRecords collection and continue polling the broker for new messages, allowing the consumer to recover from temporary broker delays.

The consumer will commit its current offset and shut down: The consumer will commit its current offset and shut down, requiring manual intervention to restart the consumer.

Answer

The consumer will return an empty ConsumerRecords collection and continue polling

Explanation

When a Kafka consumer's poll() method is called with a timeout, but the broker takes longer to respond, the consumer will return an empty ConsumerRecords collection and continue polling the broker for new messages. This allows the consumer to recover from temporary broker delays and ensures that the consumer remains responsive.

Question 12:

What is the effect of setting max.poll.records to a low value, such as 10,