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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Kafka Connect	15%	- Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns
Topic 2: Apache Kafka Fundamentals	23%	- Kafka architecture and data flow - Delivery semantics: at-most-once, at-least-once, exactly-once - Configuration basics and cluster setup - Core concepts: brokers, topics, partitions, replicas, offsets
Topic 3: Application Testing	8%	- Integration testing and test clusters - Unit testing producers and consumers - Mocking and test utilities
Topic 4: Apache Kafka Streams	12%	- Stream processing operations: map, filter, join, aggregation - Streams API concepts: topology, processors, state stores - Error handling and state management - Windowing and time semantics
Topic 5: Apache Kafka Application Development	28%	- Consumer API: configuration, offset management, consumer groups, rebalancing - Schema management: Confluent Schema Registry, compatibility rules, evolution - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence
Topic 6: Application Observability	13%	- Tracing and performance tuning - Logging and monitoring metrics - Error diagnosis and troubleshooting

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q12-Q17):

NEW QUESTION # 12
What's is true about Kafka brokers and clients from version 0.10.2 onwards?

A. A newer client can talk to a newer broker, and an older client can talk to a newer broker
B. Clients and brokers must have the exact same version to be able to communicate
C. A newer client can't talk to a newer broker, but an older client can talk to a newer broker
D. A newer client can talk to a newer broker, but an older client cannot talk to a newer broker

Answer: A

Explanation:
Kafka's new bidirectional client compatibility introduced in 0.10.2 allows this. Read more herehttps://www.
confluent.io/blog/upgrading-apache-kafka-clients-just-got-easier/

NEW QUESTION # 13
Kafka producers can batch messages going to the same partition.
Which statement is correct about producer batching?

A. Producers can only batch messages of the same size.
B. Two or more broker failures will automatically disable batching on the producer.
C. Producers can include multiple batches in a single request to a broker.
D. Producers have a separate background thread for each batch.

Answer: C

Explanation:
Kafka producers can group messages destined fordifferent partitionsintoseparate batches, and send multiple batches in one requestto optimize network usage.
FromKafka Producer Internals:
"The producer maintains buffers (batches) for each partition and can send multiple of these batches together in a single request to the broker."
* Message size is not a constraint for batching (A is false).
* Broker failures don't disable batching (B is false).
* There isno separate thread per batch(C is false).
Reference:Kafka Producer Architecture and Internals

NEW QUESTION # 14
To prevent network-induced duplicates when producing to Kafka, I should use

A. batch.size=1
B. enable.idempotence=true
C. retries=200000
D. max.in.flight.requests.per.connection=1

Answer: B

Explanation:
Producer idempotence helps prevent the network introduced duplicates. More details herehttps://cwiki.apache.
org/confluence/display/KAFKA/Idempotent+Producer

NEW QUESTION # 15
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. Log of payment transactions
B. Customer profile information
C. Catalog of products
D. Inventory of products at a warehouse
E. All purchases at a retail store occurring in real time

Answer: B,C,D

Explanation:
AGlobalKTableis a replicated, read-only table available in full on all instances. It's best forreference or lookup datasetssuch as:
* Product catalog
* Customer profiles
* Warehouse inventory
FromKafka Streams Developer Guide:
"Use GlobalKTable when you need to perform joins using non-partition-aligned reference data that's small enough to replicate."
* Purchases and transactions are high-throughput, time-sensitive streams, not static reference data.
Reference:Kafka Streams Concepts > GlobalKTable

NEW QUESTION # 16
You need to set alerts on key broker metrics to trigger notifications when the cluster is unhealthy.
Which are three minimum broker metrics to monitor?
(Select three.)

A. kafka.controller:type=KafkaController,name=ActiveControllerCount
B. kafka.controller:type=KafkaController,name=OfflinePartitionsCount
C. kafka.controller:type=KafkaController,name=LastCommittedRecordOffset
D. kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec
E. kafka.controller:type=KafkaController,name=TopicsToDeleteCount

Answer: A,B,D

Explanation:
These three metrics are critical for cluster health:
OfflinePartitionsCount: Indicates partitions without active leaders - a sign of broker failure.
ActiveControllerCount: There should be exactly one active controller. A count # 1 signals controller failure.
UncleanLeaderElectionsPerSec: Tracks leader elections where out-of-sync replicas were selected - risky for data loss.
From Kafka Monitoring Documentation:
"Offline partitions and unclean leader elections should trigger alerts. Also, ensure a single active controller is running." A is about topics pending deletion - not critical.
E is a per-topic record metric, not broker-level.
Reference: Kafka Monitoring > Key JMX Metrics

NEW QUESTION # 17
......

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