Confluent Certified Developer for Apache Kafka (CCDAK) · 30% practice allocation

Confluent CCDAK: Kafka Streams and Stream Processing Practice Questions

5 free sample questions from the Kafka Streams and Stream Processing domain, with answers and explanations. The full CCDAK bank has 58 questions across 4 domains. Questions are scenario-based and written from the official Confluent exam guide.

1.

Which Kafka Streams operation is STATELESS?

  • A. aggregate (build a running result per key)
  • B. count (aggregate the number of records per key)
  • C. reduce (combine records per key)
  • D. filter (keep records matching a predicate)✓ Correct
Explanation

filter is stateless — each record is evaluated independently against a predicate with no stored state. count (B), reduce (C), and aggregate (A) are stateful operations that maintain per-key state in a state store.

2.

Which windowing type groups records by periods of activity separated by a configurable gap of inactivity?

  • A. Tumbling windows
  • B. Sliding windows
  • C. Session windows✓ Correct
  • D. Hopping windows
Explanation

Session windows group records into sessions bounded by inactivity gaps — a new session starts when the gap is exceeded. Tumbling (A) and hopping (D) are fixed-time windows, and sliding windows (B) are defined by record-time proximity for joins/aggregations, not inactivity gaps.

3.

A design must count events in fixed, non-overlapping 5-minute intervals. Which windowing type should the developer use?

  • A. No window (global aggregation)
  • B. Session windows
  • C. Hopping windows
  • D. Tumbling windows✓ Correct
Explanation

Tumbling windows are fixed-size, non-overlapping time intervals — perfect for counting events per 5-minute bucket. Hopping windows (C) overlap, session windows (B) are gap-based around activity, and a global aggregation (A) has no time bucketing.

4.

Which statement about state stores in Kafka Streams is TRUE?

  • A. State stores replace Kafka topics entirely
  • B. Stateful operations (aggregations, joins) use local state stores that are queryable and backed by changelog topics✓ Correct
  • C. State stores cannot be queried
  • D. State stores are only available in the Schema Registry
Explanation

Stateful operations maintain local state stores that back aggregations/joins, are restorable via changelog topics, and can be exposed for interactive queries. They aren't in the Schema Registry (D), they are queryable (C), and they complement (not replace) Kafka topics (A).

5.

A hopping window is configured with a size of 10 minutes and an advance (hop) of 5 minutes. What is the effect?

  • A. Windows are 10 minutes long and start every 5 minutes, so they OVERLAP and a record can belong to multiple windows✓ Correct
  • B. Windows are non-overlapping 10-minute buckets
  • C. Windows only close on inactivity
  • D. Each record belongs to exactly one window
Explanation

A hopping window with size 10m and hop 5m produces overlapping windows every 5 minutes, so a single record can fall into more than one window. Non-overlapping buckets (B) describe tumbling windows, inactivity-based closing (C) is session windows, and single-window membership (D) is tumbling behavior.

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