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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q16-Q21):

NEW QUESTION # 16
A consumer receives a Kafka message that is serialized using an Avro schema. The consumer does not have cache locally mapping between the schema id and the schema.
What does the consumer do?

A. The consumer throws an exception because it does not have the required schema.
B. The consumer retrieves the schema from the schema registry.
C. The consumer drops do not consume the message because the mapping is not in its cache.
D. The consumer consumes the message without the schema.

Answer: B

NEW QUESTION # 17
To produce data to a topic, a producer must provide the Kafka client with...

A. the list of brokers that have the data, the topic name and the partitions list
B. any broker from the cluster and the topic name and the partitions list
C. all the brokers from the cluster and the topic name
D. any broker from the cluster and the topic name

Answer: D

Explanation:
All brokers can respond to a Metadata request, so a client can connect to any broker in the cluster and then figure out on its own which brokers to send data to.

NEW QUESTION # 18
Clients that connect to a Kafka cluster are required to specify one or more brokers in the bootstrap.servers parameter.
What is the primary advantage of specifying more than one broker?

A. It is the mechanism to distribute a topic's partitions across multiple brokers.
B. It provides redundancy in making the initial connection to the Kafka cluster.
C. It forces clients to enumerate every single broker in the cluster.
D. It provides the ability to wake up dormant brokers.

Answer: B

Explanation:
The bootstrap.servers setting is used by clients (producers and consumers) to initially connect to the Kafka cluster. It does not need to list all brokers-just a few that are up and reachable. The client will then use the cluster metadata to discover the full broker list.
From Kafka Java Client Configuration Docs:
"This is a list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping." Hence, listing multiple brokers provides redundancy and fault tolerance in the event one is down during client startup.
Reference: Apache Kafka Java Client Configuration > bootstrap.servers

NEW QUESTION # 19
In Kafka, what are Topics split into?

A. Consumers
B. Sub Topics
C. Partitions
D. Chunks

Answer: C

NEW QUESTION # 20
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. enable.auto.commit=true
B. retries=2147483647max.in.flight.requests.per.connection=5enable.idempotence=true
C. retries=0max.in.flight.requests.per.connection=5enable.idempotence=true
D. retries=2147483647max.in.flight.requests.per.connection=1enable.idempotence=false

Answer: B

Explanation:
To ensure exactly-once delivery and avoid duplicates even during retries:
enable.idempotence=true ensures deduplication on the broker
retries=2147483647 allows unlimited retries on retriable errors
max.in.flight.requests.per.connection=5 is the maximum value that preserves message order with idempotence From Kafka Producer Config Docs:
"To achieve exactly-once semantics, set enable.idempotence=true, and max.in.flight.requests.per.connection #
5."
A is unrelated (consumer-side)
C disables retries
D disables idempotence, leading to duplicates
Reference: Kafka Producer Configs > enable.idempotence, retries

NEW QUESTION # 21
......

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