Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam With Confidence Using Practice Dumps

In Spark SQL and DataFrame operations, the configuration parameter spark.sql.shuffle.partitions defines the number of partitions created during shuffle operations such as join, groupBy, and distinct.

The default value (in Spark 3.5) is 200.

If this number is too low, Spark creates fewer tasks, leading to idle executors and poor cluster utilization.

Increasing this value allows Spark to create more tasks that can run in parallel across executors, effectively using more cluster resources.

Correct approach:

spark.conf.set("spark.sql.shuffle.partitions", 400)

This increases the parallelism level of shuffle stages and improves overall resource utilization.

Why the other options are incorrect:

B: Reducing partitions further would decrease parallelism and worsen the underutilization issue.

C: Dynamic resource allocation scales executors up or down based on workload, but it doesn’t fix low task parallelism caused by insufficient shuffle partitions.

D: Increasing dataset size is not a tuning solution and doesn’t address task-level under-parallelization.

References (Databricks Apache Spark 3.5 – Python / Study Guide):

Spark SQL Configuration: spark.sql.shuffle.partitions — controls the number of shuffle partitions.

Databricks Exam Guide (June 2025): Section “Troubleshooting and Tuning Apache Spark DataFrame API Applications” — tuning strategies, partitioning, and optimizing cluster utilization.

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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

Adaptive Query Execution (AQE) in Spark 3.x automatically optimizes query plans at runtime based on the actual data characteristics observed during job execution.

Key features of AQE include:

Dynamic switching of join strategies: Changes between sort-merge join and broadcast join based on actual shuffle sizes.

Coalescing shuffle partitions: Reduces small tasks and improves parallelism efficiency.

Handling skew joins: Dynamically splits large partitions to avoid data skew.

Thus, the most accurate answer describing AQE’s function is “dynamically switching join strategies.”

Why the other options are incorrect:

B: Table statistics are collected manually or by the metastore, not by AQE.

C: AQE benefits multi-stage jobs involving shuffles, not single-stage jobs.

D: Delta file optimization is handled by Databricks utilities, not AQE.