For Kafka Streams to perform joins, topics must be co-partitioned, meaning:

They have the same number of partitions (A), and

They use the same partitioning strategy (typically by key) (B)

From Kafka Streams Documentation > Joins:

“Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner.”

Value schema (C) and retention time (D) have no bearing on join compatibility.

A 409 Conflict response in Kafka Connect REST API usually means:

The connector name provided already exists (i.e., a connector with that name is already deployed).

The Kafka Connect cluster is temporarily unavailable to accept new tasks due to a rebalance in progress.

From the Confluent Developer documentation and Kafka Connect REST API spec:

“409 – Conflict. The connector name is already in use or the worker group is in the process of rebalancing and cannot complete the request at this time.”

Option A is incorrect because invalid configurations return 400 Bad Request, not 409.

Option B is invalid because Kafka Connect does not have a strict concept of "capacity" that prevents connector creation with a 409.

The Apache Kafka Streams documentation defines a co-partitioning requirement for KStream–KTable and KStream–KStream joins. Both input topics must have the same number of partitions and the same key partitioning strategy.

One valid solution is to use a GlobalKTable (Option A). A GlobalKTable is fully replicated to every Kafka Streams instance, removing the co-partitioning requirement. This approach is recommended when the reference data is relatively small and changes infrequently.

Another valid solution is to repartition one topic so that both topics have the same number of partitions (Option B). Kafka Streams provides repartition topics specifically for this purpose, allowing proper KStream–KTable joins.

Option C does not resolve the partition mismatch, as increasing instances does not change partitioning. Option D is incorrect because Kafka Streams does not automatically repartition both topics for joins; repartitioning must be explicitly configured.

Therefore, the correct and officially supported solutions are using a GlobalKTable and explicitly repartitioning one topic.

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For proper delivery handling with acks=all:

Use callback to log or act on success/failure.

Use try/catch to handle synchronous exceptions like serialization errors or network failures.

From Kafka Producer Documentation:

“Errors can be caught either via the returned Future or via the callback interface. For fatal errors, use a try/catch block around the send call.”

Option B is incorrect because send() returns a Future, not RecordMetadata directly.

Option D is invalid — ProducerRecord has no method called status().

C is true: When a new broker is added, no partitions are assigned to it unless you create new topics or reassign existing ones using kafka-reassign-partitions.sh.

D is true: Kafka brokers are hot-pluggable; no need to stop the cluster when scaling.

From Kafka Operations Guide:

“A newly added broker won’t be assigned partitions until reassignments or new topic creation.”

“Kafka allows dynamic scaling by adding brokers without downtime.”

A is false: Broker IDs must be manually set unless using dynamic broker registration in KRaft mode.

B is false unless the cluster uses KRaft mode and the broker is specifically assigned a controller role.

To send failed records to a dead letter queue (DLQ), you must configure:

errors.tolerance=all: Tells the connector to not fail on errors but handle them (e.g., send to DLQ).

errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.

errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.

From Kafka Connect Error Handling Docs:

“Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs. Headers can include failure metadata.”

Options D, E, F are related to logging, not DLQ behavior.

Table → Product, Customers

Stream → Orders_Placed, Shipment_Of_Orders

Tables represent slowly changing, reference data — e.g., customer profiles, product info.

Streams represent real-time event data — e.g., orders placed, shipments processed.

From Kafka Streams Documentation:

“Use KTable for reference datasets, and KStream for event-based processing such as orders or logs.”

Frequent rebalances in a consumer group occur when:

Consumers are too slow and miss heartbeats (A), or

Instances crash or restart, triggering group membership changes (D)

From Kafka Consumer Group Coordination Docs:

“If a consumer fails to send a heartbeat in time (due to slowness or crash), it is removed from the group, causing a rebalance.”

Option B (partition mismatch) affects load balancing, not rebalance frequency.

Option C (broker storage) doesn't trigger consumer rebalances.

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.

SMTs (Single Message Transforms) are lightweight transformations applied to individual messages as they pass through Kafka Connect.

Chaining SMTs: You can apply multiple SMTs in sequence by defining them in order in the connector config.

Masking or modifying fields is a common use case (e.g., redacting sensitive data).

From Kafka Connect Documentation:

“Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together.”

SMTs do not perform joins (B is incorrect).

Converters and SMTs are separate concerns (D is incorrect).

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.