Confluent Certified Developer for Apache Kafka (CCDAK) — Questions and Answers

Question 1: What is Kafka Connect?

A framework for reliably streaming data between Kafka and external systems using connectors (Correct answer)
A CLI for producing test messages
A protocol for connecting two Kafka clusters
A GUI for managing Kafka topics

Correct answer: A framework for reliably streaming data between Kafka and external systems using connectors

Kafka Connect is a scalable, fault-tolerant framework that simplifies integrating Kafka with databases, storage systems, and other data sources/sinks.

Question 2: In Kafka, how many traditional message transfer techniques are available?

3
5
2 (Correct answer)
4

Correct answer: 2

Explanation: The traditional message transfer method includes two methods: queuing and delivery. Queuing is a method in which a group of consumers read a message from the server, and each message is delivered to one of them. Publish-Subscribe: In Publish-Subscribe, messages are published to all consumers.

Question 3: An application can subscribe to topics and process streams of records using Api ______ in Apache Kafka's

Streams
Connector
Consumer (Correct answer)
Producer

Correct answer: Consumer

Explanation: Consumer API — Allows an application to subscribe to topics and process records in a stream.

Question 4: How does a follower replica know it is out of sync with the leader?

When it receives a different partition ID from ZooKeeper
When its LEO lags behind the leader's LEO for more than replica.lag.time.max.ms (Correct answer)
When it fails to compress messages correctly
When it misses an ACK within acks.timeout.ms

Correct answer: When its LEO lags behind the leader's LEO for more than replica.lag.time.max.ms

A replica is removed from the ISR when it hasn't caught up with the leader's log within replica.lag.time.max.ms, indicating it is behind.

Question 5: What command lists all topics in a Kafka cluster?

kafka-admin.sh --list
kafka-topics.sh --zookeeper <host> --describe
kafka-topics.sh --bootstrap-server <host> --list (Correct answer)
kafka-configs.sh --list-topics

Correct answer: kafka-topics.sh --bootstrap-server <host> --list

kafka-topics.sh --list with --bootstrap-server connects to the cluster and lists all existing topic names.

Question 6: What type of library is Kafka Streams?

A client-side Java library for stream processing, requiring no separate cluster (Correct answer)
A separate server component bundled with Kafka
A distributed processing engine like Apache Flink
A REST API layer on top of Kafka

Correct answer: A client-side Java library for stream processing, requiring no separate cluster

Kafka Streams is a lightweight Java library embedded in your application — it uses Kafka topics as input/output and needs no additional infrastructure.

Question 7: What metric best indicates a Kafka broker is under memory pressure?

Rising ZooKeeper session timeout count
High page cache miss rate (increased disk read I/O) (Correct answer)
Increased consumer lag across groups
High GC pause frequency in the JVM

Correct answer: High page cache miss rate (increased disk read I/O)

Kafka relies heavily on the OS page cache; if the working set exceeds available memory, page cache misses cause disk reads and severely degrade throughput.

Question 8: What is the default state store backend used by Kafka Streams for persistent state?

Apache Cassandra
RocksDB (Correct answer)
H2 in-process database
LevelDB

Correct answer: RocksDB

Kafka Streams uses RocksDB as its default embedded persistent key-value store for local state in aggregations and joins.

Question 9: What is the purpose of Single Message Transforms (SMTs) in Kafka Connect?

Transform entire topic schemas
Compress messages at the broker
Convert between Avro and JSON formats in the Schema Registry
Apply lightweight transformations to individual records as they pass through the connector pipeline (Correct answer)

Correct answer: Apply lightweight transformations to individual records as they pass through the connector pipeline

SMTs are simple, chainable transformations applied to each record in-flight within a connector, allowing field renaming, filtering, routing, and other mutations.

Question 10: What is the purpose of `StreamsConfig.APPLICATION_ID_CONFIG`?

Uniquely identifies the Kafka Streams application and is used as the consumer group ID and internal topic prefix (Correct answer)
Defines the number of stream threads
Sets the Kafka broker address
Configures the state store directory

Correct answer: Uniquely identifies the Kafka Streams application and is used as the consumer group ID and internal topic prefix

The application ID serves as the consumer group ID for all internal consumers and is prepended to internal changelog and repartition topic names.

Question 11: Which consumer configuration determines how frequently the consumer offsets are committed automatically?

auto.commit.interval.ms (Correct answer)
fetch.min.bytes
session.timeout.ms
max.poll.records

Correct answer: auto.commit.interval.ms

auto.commit.interval.ms sets the frequency (in milliseconds) at which the consumer's offsets are committed when enable.auto.commit is true.

Question 12: What is the significance of a Kafka Producer API?

It is responsible for messaging
It is responsible for covering two producers (Correct answer)
It is used to commit offset
It helps to communicate between two nodes

Correct answer: It is responsible for covering two producers

Explanation: The major role of the Kafka Producer API is that it is responsible for covering two producers.

Question 13: Which setting must be enabled for a Kafka consumer to commit offsets automatically?

enable.auto.commit=true (Correct answer)
isolation.level=read_committed
fetch.wait.max.ms=500
auto.offset.reset=earliest

Correct answer: enable.auto.commit=true

enable.auto.commit=true instructs the consumer to automatically commit offsets at the interval specified by auto.commit.interval.ms.

Question 14: What Kafka configuration property specifies the security protocol for a listener?

listener.security.protocol.map (Correct answer)
ssl.keystore.location
security.inter.broker.protocol
sasl.mechanism

Correct answer: listener.security.protocol.map

listener.security.protocol.map maps each named listener to its security protocol (PLAINTEXT, SSL, SASL_PLAINTEXT, or SASL_SSL).

Question 15: How do Dead Letter Queues (DLQs) work in Kafka Connect sink connectors?

All records are written to a DLQ before processing
Records that fail to be processed are routed to a configurable DLQ topic instead of stopping the connector (Correct answer)
DLQs store connector configuration errors
Failed records cause the connector to pause automatically

Correct answer: Records that fail to be processed are routed to a configurable DLQ topic instead of stopping the connector

A DLQ allows a sink connector to continue processing by redirecting problematic records to a separate topic, preventing one bad record from halting the pipeline.

Question 16: Consumers utilize Apache Kafka's control records to

compact all transactional messages
filter out aborted transactional messages (Correct answer)
control all transactional messages

Correct answer: filter out aborted transactional messages

Explanation: Control records in Apache Kafka are used by consumers to filter out aborted transactional messages.

Question 17: Which Kafka Streams config controls the number of parallel stream processing threads per instance?

max.poll.threads
num.stream.threads (Correct answer)
parallelism.factor
stream.concurrency

Correct answer: num.stream.threads

num.stream.threads sets how many threads within a single Kafka Streams application instance process stream tasks concurrently.

Question 18: What is the 'page cache' and why is it important for Kafka performance?

A client-side cache for recently consumed offsets
A Kafka-specific caching layer in front of ZooKeeper
An in-memory buffer inside each broker JVM for recent messages
The OS-level disk cache that Kafka relies on to serve repeated reads without physical disk I/O (Correct answer)

Correct answer: The OS-level disk cache that Kafka relies on to serve repeated reads without physical disk I/O

Kafka intentionally avoids maintaining its own in-process cache and instead relies on the OS page cache, allowing hot data to be served from memory by the kernel.

Question 19: What is the role of the Kafka broker's 'log cleaner' thread?

Compresses log segment files
Truncates follower logs during leader election
Performs log compaction by removing older duplicate-key records from compacted topics (Correct answer)
Deletes expired messages based on retention.ms

Correct answer: Performs log compaction by removing older duplicate-key records from compacted topics

The log cleaner is a background thread that runs compaction on topics with cleanup.policy=compact, merging segments and removing superseded key records.

Question 20: What is the function of the Kafka `__transaction_state` internal topic?

Logs all broker configuration changes
Stores compacted key offsets for cleanup
Stores the state of ongoing and completed transactions managed by transaction coordinators (Correct answer)
Tracks consumer group rebalance events

Correct answer: Stores the state of ongoing and completed transactions managed by transaction coordinators

__transaction_state is Kafka's internal topic for persisting transaction status so that transaction coordinators can recover after a failure.

Question 21: What is the difference between a 'source connector' and a 'sink connector' in Kafka Connect?

Source connectors ingest data into Kafka; sink connectors export data from Kafka to external systems (Correct answer)
Source is for streams; sink is for batch
Source handles keys; sink handles values
Source reads from Kafka; sink writes to Kafka

Correct answer: Source connectors ingest data into Kafka; sink connectors export data from Kafka to external systems

Source connectors pull data from external systems (like databases) into Kafka topics, while sink connectors push data from Kafka topics to external systems.

Question 22: Which Kafka Connect mode is most suitable for development/testing with a single worker?

Embedded mode
Distributed mode
Standalone mode (Correct answer)
Local mode

Correct answer: Standalone mode

Standalone mode runs a single Connect worker process with connectors defined in property files, ideal for development and simple deployments.

Question 23: Which CLI tool is used to list, describe, and reset consumer group offsets in Kafka?

kafka-consumer-groups.sh (Correct answer)
kafka-group-manager.sh
kafka-offsets.sh
kafka-console-consumer.sh

Correct answer: kafka-consumer-groups.sh

kafka-consumer-groups.sh manages consumer group metadata including listing groups, describing lag, resetting offsets, and deleting groups.

Question 24: Which command-line tool is used to create, delete, describe, and alter Kafka topics?

kafka-admin.sh
kafka-console-producer.sh
kafka-cluster.sh
kafka-topics.sh (Correct answer)

Correct answer: kafka-topics.sh

kafka-topics.sh is the standard CLI tool for managing Kafka topics including create, delete, alter, and describe operations.

Question 25: What is the purpose of Kafka's `__consumer_offsets` topic compaction?

To archive old consumer offsets to cold storage
To replicate offsets to ZooKeeper
To retain only the latest committed offset per consumer group and partition, keeping the topic size manageable (Correct answer)
To merge multiple consumer groups

Correct answer: To retain only the latest committed offset per consumer group and partition, keeping the topic size manageable

Log compaction on __consumer_offsets ensures that only the most recent offset commit per (group, topic, partition) key is retained, preventing unbounded growth.

Question 26: In Kafka, why is replication required? Because it guarantees...

A published message will not be deleted
A published message will not be lost (Correct answer)
A published message will not be saved
A published message will not be sent

Correct answer: A published message will not be lost

Explanation: In any case, Kafka ensures that no data is lost.

Question 27: An application can publish streams of records using Apache Kafka's ______ Api.

Consumer
Producer (Correct answer)
Streams
Connector

Correct answer: Producer

Explanation: Producer API — Allows programs to publish records in streams.

Question 28: What does SASL/OAUTHBEARER allow in Kafka authentication?

Clients authenticate using OAuth 2.0 bearer tokens obtained from an external authorization server (Correct answer)
Clients use mutual TLS certificates
Clients authenticate with username/password stored in ZooKeeper
Clients use Kerberos tickets from an Active Directory

Correct answer: Clients authenticate using OAuth 2.0 bearer tokens obtained from an external authorization server

SASL/OAUTHBEARER enables Kafka clients to authenticate using short-lived OAuth 2.0 tokens, integrating with modern identity providers.

Question 29: What is the default value for the `log.retention.hours` configuration in Apache Kafka?

168 hours (7 days) (Correct answer)
48 hours
720 hours (30 days)
24 hours

Correct answer: 168 hours (7 days)

The default log.retention.hours is 168 hours (7 days), after which messages are eligible for deletion by the log cleaner.

Question 30: What does `auto.offset.reset=earliest` do for a new consumer group?

Reads from the very beginning of the topic (Correct answer)
Reads from the middle partition
Throws an exception if no offset exists
Reads only new messages

Correct answer: Reads from the very beginning of the topic

earliest causes the consumer to start reading from the earliest available offset when no committed offset exists for the group.

Question 31: What is the purpose of the replication factor in a Kafka topic?

It controls the number of partitions
It determines compression type
It sets the maximum message size
It defines how many copies of each partition exist across brokers for fault tolerance (Correct answer)

Correct answer: It defines how many copies of each partition exist across brokers for fault tolerance

The replication factor specifies how many broker copies exist for each partition, ensuring data durability if a broker fails.

Question 32: What is a 'repartition topic' in Kafka Streams and when is it created?

A topic used to replicate changelog data
A topic created when joining two KTables
A topic created for each state store
An internal topic created when a key-changing operation (e.g. selectKey) precedes a join or aggregation requiring co-partitioning (Correct answer)

Correct answer: An internal topic created when a key-changing operation (e.g. selectKey) precedes a join or aggregation requiring co-partitioning

When an operation changes the record key, Kafka Streams must repartition the data via an internal topic to ensure co-partitioned data lands on the same task.

Question 33: What is a 'preferred replica election' in Kafka?

An election that selects the fastest replica as leader
Automatic rebalancing of topic leaders across brokers
A manual ACL grant for replica access
A process that restores partition leadership to the originally assigned (preferred) broker after a failover (Correct answer)

Correct answer: A process that restores partition leadership to the originally assigned (preferred) broker after a failover

After a broker failure and recovery, preferred replica election moves leadership back to the originally assigned broker to achieve balanced load distribution.

Question 34: What happens to a failed Connect task if you POST to `/connectors/{name}/tasks/{taskId}/restart`?

The entire connector is recreated
The specific task is restarted and attempts to resume from its last stored offset (Correct answer)
A new connector with a fresh offset is created
The worker hosting the task is rebooted

Correct answer: The specific task is restarted and attempts to resume from its last stored offset

Restarting an individual task via the REST API causes that task to reinitialize and resume data copying from the last committed offset.

Question 35: What is a 'tumbling window' in Kafka Streams?

A sliding window that moves forward by a fixed step
A session window defined by inactivity gaps
An unbounded window that aggregates all records
A fixed-size, non-overlapping time window where each record belongs to exactly one window (Correct answer)

Correct answer: A fixed-size, non-overlapping time window where each record belongs to exactly one window

Tumbling windows partition time into equal-sized, non-overlapping buckets so each event falls into exactly one window.

Question 36: When was Apache Kafka initially released?

2006
2011 (Correct answer)
2001
2010

Correct answer: 2011

Explanation: Apache Kafka was first released by its original author(s) at LinkedIn in January 2011 - and was immediately open sourced after that. It is still open source and maintained by the Apache Software Foundation's developers.

Question 37: What is a 'session window' in Kafka Streams?

A window tied to the user's login session
A window that resets every hour
A fixed 30-minute window
A dynamic window that groups records separated by an inactivity gap smaller than a configurable threshold (Correct answer)

Correct answer: A dynamic window that groups records separated by an inactivity gap smaller than a configurable threshold

Session windows group events for a key into a session as long as the gap between consecutive events is less than the inactivity gap timeout.

Question 38: What is 'zero-copy' in the context of Kafka's I/O performance?

Data is transferred from disk to network buffer directly in the OS kernel without copying through user space (Correct answer)
The producer reuses message objects without copying
Messages are stored without serialization overhead
No replicas means no copy overhead

Correct answer: Data is transferred from disk to network buffer directly in the OS kernel without copying through user space

Kafka uses the OS sendfile() system call to transfer data from the page cache to the network socket entirely in kernel space, dramatically reducing CPU and memory overhead.

Question 39: What does a 'stream-table join' in Kafka Streams do?

Merges two streams by timestamp
Joins two tables producing a new table
Aggregates stream records into table format
Enriches each stream record with the current table value for the matching key (Correct answer)

Correct answer: Enriches each stream record with the current table value for the matching key

A stream-table join looks up the latest KTable value for a record's key and enriches the stream record with that value in real time.

Question 40: What HTTP endpoint checks the status of a running connector in Kafka Connect's REST API?

PUT /connectors/{name}/config
GET /connectors/{name}/tasks
POST /connectors/{name}/restart
GET /connectors/{name}/status (Correct answer)

Correct answer: GET /connectors/{name}/status

GET /connectors/{name}/status returns the current state of the connector and each of its tasks (RUNNING, PAUSED, FAILED, etc.).

Question 41: What is a Kafka 'transaction'?

An atomic operation that writes to multiple topics/partitions and commits consumer offsets as a single all-or-nothing unit (Correct answer)
A distributed transaction spanning external databases
A scheduled batch job in Kafka
A ZooKeeper lock acquired during produce

Correct answer: An atomic operation that writes to multiple topics/partitions and commits consumer offsets as a single all-or-nothing unit

Kafka transactions allow producers to send messages to multiple partitions and commit consumer offsets atomically, ensuring all-or-nothing semantics.

Question 42: What does the `auto.create.topics.enable` broker configuration setting control?

Whether Kafka automatically rebalances partitions across new brokers
Whether consumers can create private topics for internal use
Whether Kafka automatically adjusts the replication factor for under-replicated topics
Whether Kafka automatically creates a topic when it is first referenced by a producer or consumer (Correct answer)

Correct answer: Whether Kafka automatically creates a topic when it is first referenced by a producer or consumer

When auto.create.topics.enable=true, Kafka creates a topic automatically the first time it is referenced, using default partition and replication settings.

Question 43: What does the `advertised.listeners` broker configuration do?

Configures internal replication addresses
Sets the ZooKeeper node path for the broker
Publishes the listener addresses that clients should use to connect to the broker (Correct answer)
Enables broker metrics advertised to Prometheus

Correct answer: Publishes the listener addresses that clients should use to connect to the broker

advertised.listeners tells Kafka what addresses to publish in cluster metadata so clients can reach the broker, critical in containerized or cloud environments.

Question 44: What does the 'unclean.leader.election.enable' setting control?

Whether leader elections are logged
Compaction behavior during leader change
Whether an out-of-sync replica can be elected leader, risking data loss (Correct answer)
Whether followers can read directly

Correct answer: Whether an out-of-sync replica can be elected leader, risking data loss

When set to true, unclean.leader.election.enable allows a replica not in the ISR to become leader, potentially losing messages that were not yet replicated.

Question 45: What is the Schema Registry's role when used with Kafka Connect?

Centrally stores and validates Avro/JSON/Protobuf schemas so producers and consumers share a consistent contract (Correct answer)
Manages connector JAR deployments
Tracks consumer group offset history
Stores broker SSL certificates

Correct answer: Centrally stores and validates Avro/JSON/Protobuf schemas so producers and consumers share a consistent contract

The Schema Registry stores schemas by subject so that Connect converters can serialize and deserialize messages with schema versioning and compatibility enforcement.

Question 46: Why does Kafka store messages on disk sequentially?

Sequential storage enables built-in encryption
Disk is cheaper than RAM
It avoids the need for a file index
Sequential disk writes are extremely fast and leverage OS prefetching, making them comparable to in-memory speeds (Correct answer)

Correct answer: Sequential disk writes are extremely fast and leverage OS prefetching, making them comparable to in-memory speeds

Sequential I/O on modern hardware is orders of magnitude faster than random I/O; Kafka's append-only log design exploits this along with OS read-ahead prefetching.

Question 47: What is the Kafka 'high-water mark' (HWM)?

The last committed consumer group offset
The maximum lag threshold before alerting
The highest offset that has been replicated to all in-sync replicas and is safe to expose to consumers (Correct answer)
The maximum bytes a broker stores before truncating

Correct answer: The highest offset that has been replicated to all in-sync replicas and is safe to expose to consumers

The high-water mark is the offset up to which all ISR replicas have acknowledged data; consumers can only read up to this offset to ensure consistency.

Question 48: How do you reset a consumer group's offsets to the earliest available message using the CLI?

kafka-offsets.sh --reset --start-earliest
kafka-consumer-groups.sh --reset-offsets --to-earliest --execute (Correct answer)
kafka-console-consumer.sh --from-beginning --reset
kafka-topics.sh --reset-offsets --to-earliest

Correct answer: kafka-consumer-groups.sh --reset-offsets --to-earliest --execute

kafka-consumer-groups.sh with --reset-offsets --to-earliest --execute resets all offsets for a consumer group to the beginning of each partition.

Question 49: How does Kafka KRaft mode differ from the ZooKeeper-based architecture?

KRaft mode eliminates ZooKeeper by storing cluster metadata in a Kafka internal topic managed by a Raft consensus quorum (Correct answer)
KRaft mode requires a dedicated metadata broker tier
KRaft uses an external etcd cluster instead of ZooKeeper
KRaft is a client-side caching layer

Correct answer: KRaft mode eliminates ZooKeeper by storing cluster metadata in a Kafka internal topic managed by a Raft consensus quorum

KRaft (Kafka Raft) replaces ZooKeeper with Kafka's own built-in Raft consensus protocol for metadata management, simplifying operations.

Question 50: What is the default Kafka broker port?

2181
9092 (Correct answer)
8080
9093

Correct answer: 9092

Kafka brokers listen on port 9092 by default for PLAINTEXT client connections (port 9093 is common for SSL).

Question 51: What is the impact of increasing the number of partitions on a topic that already has consumers?

Consumers are automatically updated with no interruption
It only affects new consumer groups
It triggers a consumer group rebalance as partitions are redistributed (Correct answer)
Partition increase requires topic deletion and recreation

Correct answer: It triggers a consumer group rebalance as partitions are redistributed

Adding partitions to a topic causes the group coordinator to initiate a rebalance so that the new partitions are assigned to consumer group members.

Question 52: What is a Kafka 'leader' partition?

The broker that manages ZooKeeper metadata
The single replica that handles all reads and writes for a partition (Correct answer)
The oldest replica across brokers
The replica with the highest offset

Correct answer: The single replica that handles all reads and writes for a partition

Each partition has exactly one leader replica that handles all producer writes and (by default) consumer reads; followers replicate from it.

Question 53: What is a Kafka 'offset'?

A broker-assigned message UUID
The number of bytes from the start of a log file
A consumer group's processing delay metric
A sequential integer that uniquely identifies each message's position within a partition (Correct answer)

Correct answer: A sequential integer that uniquely identifies each message's position within a partition

An offset is a monotonically increasing integer assigned to each message within a partition, used by consumers to track which messages have been read.

Question 54: What is a Kafka 'broker rack' configuration used for?

Groups brokers for load-balanced consumer assignment
Assigns a broker to a rack/availability zone so partition replicas can be spread across failure domains (Correct answer)
Defines the physical server rack for cable management
Controls network bandwidth per broker

Correct answer: Assigns a broker to a rack/availability zone so partition replicas can be spread across failure domains

The broker.rack setting tells Kafka to spread partition replicas across different racks or AZs so that a single rack failure won't take down all replicas of a partition.

Question 55: What is the role of `buffer.memory` in a Kafka producer?

JVM heap allocation for the producer
Total bytes of memory the producer uses to buffer records waiting to be sent (Correct answer)
Memory reserved for compression
Size of each message batch

Correct answer: Total bytes of memory the producer uses to buffer records waiting to be sent

buffer.memory is the total amount of memory the producer allocates for buffering records before they are transmitted to brokers.

Question 56: Kafka was created by which organization?

Microsoft
Goggle
Linkedln (Correct answer)
Amazon

Correct answer: Linkedln

Explanation: LinkedIn created Kafka in 2009, but it was eventually outsourced to the Apache Software Foundation in 2011. In a word, Kafka is a distributed streaming technology with high volume, high throughput, super scalability, and reliability.

Question 57: Which topic-level configuration controls the maximum size a log segment file can reach before a new one is created?

log.segment.bytes (Correct answer)
log.retention.bytes
log.roll.ms
segment.index.bytes

Correct answer: log.segment.bytes

log.segment.bytes (default 1 GB) determines the maximum size of a single log segment file before Kafka rolls over to a new segment.

Question 58: What mode allows Kafka Connect to run as part of a distributed cluster for fault tolerance?

Standalone mode
Replicated mode
Distributed mode (Correct answer)
Cluster mode

Correct answer: Distributed mode

In distributed mode, multiple Connect workers form a group, share connector tasks, and automatically rebalance when a worker joins or leaves.

Question 59: What does the `filter()` operation do in a Kafka Streams topology?

Splits the stream into multiple sub-streams
Transforms each record's value
Joins two streams on a common key
Passes only records that satisfy a given predicate, dropping the rest (Correct answer)

Correct answer: Passes only records that satisfy a given predicate, dropping the rest

filter() evaluates a Predicate for each record and only forwards records where the predicate returns true.

Question 60: What does the Kafka log-end offset (LEO) represent?

The last offset consumed by all consumer groups
The high-water mark for the partition
The first offset in the current active segment
The next offset to be written, i.e., the offset of the last record + 1 in a replica's log (Correct answer)

Correct answer: The next offset to be written, i.e., the offset of the last record + 1 in a replica's log

The log-end offset is the offset of the next message to be appended; it represents the tip of a replica's local log and may exceed the high-water mark on followers.

Confluent Certified Developer for Apache Kafka (CCDAK)

The CCDAK certifies proficiency in building, deploying, and managing Apache Kafka applications, covering core Kafka fundamentals, producers/consumers, Kafka Streams, Kafka Connect, and application observability.

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90 min

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70%

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