Confluent CCDAK : Confluent Certified Developer for Apache Kafka Certification Examination

Exam Code: CCDAK

Exam Name: Confluent Certified Developer for Apache Kafka Certification Examination

Updated: Oct 05, 2026

Q & A: 92 Questions and Answers

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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Developing with Kafka Producers	15-20%	- Producer Interceptors - Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Partitioning Strategies - Error Handling and Retry Logic - Idempotent Producers - Exactly-Once Semantics
Topic 2: Kafka Connect	10-15%	- Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Single Message Transformations (SMT) - Offset Management in Connect - Source and Sink Connectors - Connector Configuration
Topic 3: Developing with Kafka Consumers	15-20%	- Consumer Configuration and Tuning - Offset Management - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) - Deserialization - Consumer Interceptors - Consumer Groups and Rebalancing
Topic 4: Security	5-10%	- Securing Schema Registry - Encryption in Transit - Authentication (SASL, SSL/TLS) - Authorization (ACLs)
Topic 5: Schema Management	5-10%	- Schema Registry Architecture - Schema Registration and Retrieval - Avro Schemas - Schema Evolution and Compatibility - Protobuf and JSON Schemas
Topic 6: Kafka Fundamentals	10-15%	- Log Segments and Retention - Topics, Partitions, and Offsets - Replication Factor and ISR - Brokers and Clusters - Producer and Consumer Basics
Topic 7: Kafka Streams	20-25%	- Transformation Operations (map, filter, join, aggregate) - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Streams Architecture and Topology - Exactly-Once Processing - Interactive Queries - Testing Kafka Streams Applications
Topic 8: Monitoring and Operations	5-10%	- Log Compaction - Consumer Lag and Health Checks - Kafka Metrics and Monitoring - Cross-region Replication - Disaster Recovery
Topic 9: Installing and Configuring Kafka	5-10%	- Partition Assignment Strategies - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:

Question #1

Match each configuration parameter with the correct option.
To answer choose a match for each option from the drop-down. Partial
credit is given for each correct answer.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
Correct Matches
* path.format#Only valid for some Source Connectors
* value.converter#Valid for all Source Connectors
* input.path#Only valid for some Source Connectors
* errors.retry.timeout#Valid for all Source Connectors
* tasks.max#Valid for all Source Connectors
* path.formatThis is a connector-specific configuration, commonly used by file-based or storage-based source connectors (for example, HDFS, S3, or FilePulse connectors). It isnot part of the Kafka Connect framework itself, so it is only valid for some source connectors.
* value.converterThis is aworker-level configurationthat defines how record values are converted (JSON, Avro, Protobuf, etc.). It applies toall connectorsrunning on the worker unless overridden at the connector level.
* input.pathThis configuration is specific to file-based source connectors (e.g., FileStreamSourceConnector). It isnot universally supported, so it is only valid for some source connectors.
* errors.retry.timeoutThis is part of Kafka Connect'serror handling framework, introduced to support retries and tolerance of transient errors. It is ageneric Connect configurationand valid for all connectors.
* tasks.maxThis is amandatory, generic connector configurationthat controls the maximum number of tasks a connector may create. It is valid forall source (and sink) connectors.

Question #2

(You want to read messages from all partitions of a topic in every consumer instance of your application.
How do you do this?)

A. Use the subscribe() method with an empty consumer group name configuration.
B. Use the assign() method using all topic-partitions of the topic as argument.
C. Use the subscribe() method with a regular expression argument.
D. Use the assign() method with the topic name as argument.

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #3

You are writing to a topic with acks=all.
The producer receives acknowledgments but you notice duplicate messages.
You find that timeouts due to network delay are causing resends.
Which configuration should you use to prevent duplicates?

A. retries=2147483647max.in.flight.requests.per.connection=5enable.idempotence=true
B. enable.auto.commit=true
C. retries=2147483647max.in.flight.requests.per.connection=1enable.idempotence=false
D. retries=0max.in.flight.requests.per.connection=5enable.idempotence=true

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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Question #4

You are building a system for a retail store selling products to customers.
Which three datasets should you model as a GlobalKTable?
(Select three.)

A. Catalog of products
B. All purchases at a retail store occurring in real time
C. Customer profile information
D. Log of payment transactions
E. Inventory of products at a warehouse

Reveal Solution Discussion 0

Correct Answer: A,C,E 🗳️

Explanation: Only visible for PassTorrent members. You can sign-up / login (it's free).

Question #5

Match the topic configuration setting with the reason the setting affects topic durability.
(You are given settings like unclean.leader.election.enable=false, replication.factor, min.insync.replicas=2)

Reveal Solution Discussion 0

Correct Answer:

Explanation:
unclean.leader.election.enable=false # Prevents data loss by only considering in-sync replicas when rebalancing.
replication.factor # Specifies how many redundant copies of partitions are distributed across brokers.
min.insync.replicas=2 # Sets the standard for the number of partition instances that must keep up with the latest committed message.
unclean.leader.election.enable=false ensures that only in-sync replicas can be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.
replication.factor defines how many brokers will maintain copies of each partition, directly impacting durability and availability.
min.insync.replicas determines how many replicas must acknowledge a write when acks=all is used, enforcing write durability.
Reference: Apache Kafka Topic Configuration Documentation

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