Free Confluent CCDAK Practice Exam with Questions & Answers

For SASL/PLAIN authentication over SSL, the correct combination is:

security.protocol=SASL_SSL

sasl.jaas.config=...PlainLoginModule required username=... password=...

From Kafka Security Documentation:

“For SASL/PLAIN over SSL, configure security.protocol=SASL_SSL and provide login module configuration using sasl.jaas.config.”

Options B and D are incorrect because they do not pair the correct protocol with the authentication method.

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.

According to the Apache Kafka consumer and scalability documentation, parallelism in message processing is primarily determined by the number of partitions and the number of consumers within a consumer group. Each partition can be assigned to only one consumer at a time. In this scenario, the topic already has four partitions, but only two consumers are processing them, meaning only two partitions are actively consumed in parallel.

Since monitoring indicates that message processing is CPU-bound, the bottleneck is not Kafka I/O but application-side processing. Adding more consumers (up to the number of partitions) allows Kafka to rebalance the partitions and distribute them across more consumer instances, increasing CPU parallelism and reducing lag.

Option A (adding more partitions) can increase parallelism but is more intrusive and may require repartitioning considerations for keys and downstream systems. Option B increases batch size, which can worsen CPU pressure. Option D may reduce per-poll workload but does not increase parallel processing capacity.

Therefore, the correct and recommended action per Kafka documentation is to add more consumers to the consumer group.

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The Kafka Streams DSL provides three primary abstractions:

KStream: Represents an unbounded stream of records (append-only).

KTable: Represents a changelog stream where each key has the latest value (like a materialized view).

GlobalKTable: A fully replicated version of KTable across all stream instances, enabling global joins.

From Kafka Streams Developer Guide:

“Kafka Streams provides three core abstractions: KStream, KTable, and GlobalKTable, each serving different purposes in stream processing and joins.”

GlobalKStream and StreamTable do not exist in Kafka’s API.

To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.

From Kafka Consumer Error Handling Docs:

“On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record.”

A does not prevent re-processing the bad record.

C is invalid; there's no skip() method in the Kafka consumer API.

D results in service interruption — not ideal for resiliency.

Log compaction ensures that Kafka retains at least the latest value per key in a topic. Compaction happens in the background and removes older records with the same key in inactive log segments, not immediately.

From the Kafka Documentation > Log Compaction:

“Kafka guarantees that the last message for each key will be retained in the log after compaction, even if earlier messages with the same key are deleted.”

So, D is correct. A is incorrect because compaction does not overwrite; it's a background process. B is incorrect—deletion is not immediate. C incorrectly suggests client-side hashmap behavior.

Using assign() gives manual control over partition assignment. In this mode:

B is true: The application chooses partitions directly without involving the group coordinator.

C is true: Since no group coordination occurs, no rebalancing happens if a consumer dies.

From Kafka Consumer API Docs:

“assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered.”

A is false: subscribe() and assign() are mutually exclusive.

D is incorrect: Partitions don't need to be equal across topics.

According to the official Apache Kafka documentation, log compaction is the mechanism used to retain only the latest record for each key in a topic. By setting cleanup.policy=compact, Kafka ensures that older records with the same key are eventually removed, leaving only the most recent value for each key.

This behavior is exactly suited for use cases such as tracking the current state of an entity, including GPS tracker locations, user profiles, or configuration data. In this scenario, the key (trackerId) uniquely identifies a tracker, and the value represents its most recent latitude and longitude.

Option B (retention.ms=infinite) retains all historical data, which contradicts the requirement. Option C (min.cleanable.dirty.ratio) controls when compaction runs, not what data is retained. Option D (retention.ms=0) would immediately delete all data and is unsafe.

Therefore, setting cleanup.policy=compact is the correct and officially documented solution to always retain the latest location per tracker.

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For real-time applications, the key metric to monitor is consumer lag — the difference between the latest offset on the broker and the last committed offset by the consumer.

From Kafka Monitoring Guide:

“Consumer lag is the most important metric for real-time applications. It tells you how far behind the consumer is from the latest data in Kafka.”

B relates to replication, not client responsiveness.

C tracks group membership stability, not data delay.

D tracks input throughput, not processing latency.

According to the official Apache Kafka Streams documentation, session windows are specifically designed to model periods of activity separated by gaps of inactivity. A session window groups events that occur close together in time and closes when no new events arrive within a defined inactivity gap.

In this use case, tracking user shopping cart behavior requires detecting when a user becomes inactive (for example, stops adding or removing items). Session windows naturally represent this pattern by creating a new window when activity resumes after a period of silence. This makes them ideal for modeling user sessions, cart abandonment detection, and inactivity analysis.

Hopping and tumbling windows (Options B and C) are fixed-time windows and are not well-suited for detecting inactivity, as they segment data based on clock time rather than behavior. Sliding windows are primarily used for continuous joins and do not explicitly model inactivity gaps.

Therefore, session windows are the correct and officially recommended choice for identifying periods of user inactivity in Kafka Streams applications.

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