Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.5 – Python

Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.

Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault-tolerance.

trigger(availableNow=True) is a batch-style trigger, not suited for low-latency streaming.

So:

Option A uses micro-batching with a tight trigger interval → minimal latency + exactly-once guarantee.

Final Answer: A

Spark Connect introduces a decoupled client-server architecture. Its key feature is enabling Spark job submission and execution from remote clients — in Python, Java, etc.

From Databricks documentation:

“Spark Connect allows remote clients to connect to a Spark cluster and execute Spark jobs without being co-located with the Spark driver.”

A is close, but "any language" is overstated (currently supports Python, Java, etc., not literally all).

B refers to REST, which is not Spark Connect's mechanism.

D is incorrect; Spark Connect isn’t focused on ingestion.

Final Answer: C

Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:

spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold

then Spark prefers to use a shuffled hash join.

From the Spark documentation:

“AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins.”

Therefore:

A is wrong — Cartesian joins are only used with no join condition.

B is correct — this is the optimized join for small partitioned shuffle data under AQE.

C and D are used under other scenarios but not for this case.

Final Answer: B