Pass the Confluent Certified Developer CCDAK Questions and answers with Dumpstech

To maintain message ordering per bank account, the message key must be the account number. Kafka guarantees ordering per partition, and the partition is selected based on the hash of the key.

From Kafka Producer Docs:

“All records with the same key will be sent to the same partition, and Kafka preserves the order of records in a partition.”

Using account number ensures:

All messages for the same account go to the same partition

Order is maintained

Workload is evenly distributed if account keys are varied

Other options break ordering or introduce unnecessary randomness.

If the application is CPU-bound and lagging, adding more consumers to the group will allow better parallel processing, especially since the topic has 4 partitions, allowing up to 4 active consumers.

From Kafka Consumer Group Docs:

“Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized.”

B may help but requires repartitioning and coordination.

C or D affects how much data is polled, not how fast it’s processed.

Low I/O thread activity with blocked throughput often indicates that producer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.

From Kafka Producer Performance Guide:

“Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput.”

Compression (A) may slightly impact CPU but not I/O thread usage.

Large batches (B) improve throughput if managed correctly.

A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.

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.

In the Kafka Streams API, operations are classified as stateless or stateful based on whether they require maintaining local state stores. According to the official Kafka Streams documentation, stateless operations process each record independently, without storing or accessing prior records.

Filter is a stateless operation because it evaluates each record individually and decides whether to pass it downstream. It does not require any state or historical context.

GroupBy is also considered stateless because it merely repartitions the stream by assigning a new key and forwarding records to downstream processors. While it triggers the creation of an internal repartition topic, the GroupBy operation itself does not maintain a state store.

In contrast, Reduce is a stateful operation because it aggregates records over time and requires maintaining intermediate results in a state store. Similarly, Join operations are stateful because Kafka Streams must buffer and store records from one or both input streams or tables to perform the join within a defined time window.

Thus, the correct stateless operations are Filter and GroupBy, as documented in the Kafka Streams developer guide.

According to the official Apache Kafka Connect documentation, RegexRouter is the SMT specifically designed to dynamically change the destination topic name of records produced by a connector. It works by applying a regular expression to the original topic name and rewriting it to a new topic name.

This makes RegexRouter the correct choice when some records must be routed to a different topic, typically in combination with connector-level logic, predicates, or multiple connectors. It is commonly used for topic renaming, topic versioning, or routing records to alternate topics.

InsertField (Option B) only adds metadata fields (such as topic, partition, or timestamp) to the record payload and does not affect routing. TombstoneHandler (Option C) is used to manage null-value records, especially with compacted topics. HeaderFrom (Option D) copies fields into headers but does not change the target topic.

Therefore, RegexRouter is the only SMT that directly supports changing the output topic, as documented in the Kafka Connect SMT reference.

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According to the official Apache Kafka Streams documentation, Kafka Streams relies on internal topics (such as changelog topics and repartition topics) to store state, task assignments, and application metadata. These topics are essential for fault tolerance, rebalancing, and state recovery.

When auto-topic creation is disabled, Kafka Streams cannot automatically create the required internal topics unless they already exist. In this situation, the Streams application will fail during startup with a fatal, non-retriable exception, indicating that the necessary internal topics could not be created.

Kafka Streams does not pause or wait for manual topic creation, nor does it operate without storing metadata. Instead, it explicitly requires these topics to exist or be creatable. While administrators can manually pre-create the required internal topics with the correct configuration, failure to do so causes the application to terminate.

Therefore, the correct and documented behavior is that the Kafka Streams application terminates with a non-retriable exception when auto-topic creation is disabled and required internal topics are missing.