Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps

The repartition(n) transformation reshuffles data into exactly n partitions.

Unlike coalesce(), repartition() always causes a shuffle to evenly redistribute the data.

Correct behavior:

df2 = df.repartition(20)

df2.rdd.getNumPartitions() # returns 20

Thus, the resulting DataFrame will have 20 partitions.

Why the other options are incorrect:

A/D: Doesn't retain old partition count --- it's explicitly set to 20.

C: Number of partitions is not automatically tied to executors.

PySpark DataFrame API --- repartition() vs. coalesce().

Databricks Exam Guide (June 2025): Section ''Developing Apache Spark DataFrame/DataSet API Applications'' --- tuning partitioning and shuffling for performance.

Apache Spark is a distributed data processing engine designed for large-scale, cluster-based computation.

Advantages:

Horizontal Scalability: Spark can distribute tasks across many machines, handling datasets larger than the memory of a single node.

Fault Tolerance: Spark automatically recovers from node or task failures using the lineage graph (RDD recovery mechanism) and retry logic.

These two features allow Spark to process huge datasets efficiently and reliably, unlike standard Python scripts that are limited to one machine and fail on single-node errors.

Why the other options are incorrect:

B: Spark runs on commodity hardware; no specialized machines required.

C: Spark emphasizes in-memory processing, not disk-only operations.

D: Spark still requires user code in Python, Scala, SQL, or Java.

Databricks Exam Guide (June 2025): Section ''Apache Spark Architecture and Components'' --- advantages, cluster execution, and fault tolerance.

Apache Spark Overview --- distributed processing and resilience design.

In Apache Spark, the execution hierarchy is structured as follows:

Application: The highest-level unit, representing the user program built on Spark.

Job: Triggered by an action (e.g., collect(), count()). Each action corresponds to a job.

Stage: A job is divided into stages based on shuffle boundaries. Each stage contains tasks that can be executed in parallel.

Task: The smallest unit of work, representing a single operation applied to a partition of the data.

When the collect() action is invoked, Spark initiates a job. This job is then divided into stages at points where data shuffling is required (i.e., wide transformations). Each stage comprises tasks that are distributed across the cluster's executors, operating on individual data partitions.

This hierarchical execution model allows Spark to efficiently process large-scale data by parallelizing tasks and optimizing resource utilization.

Underutilization with timeout warnings often indicates insufficient parallelism --- meaning there aren't enough executors to process all tasks concurrently.

Solution:

Increase the number of executors to allow more parallel task execution and better resource utilization.

Example configuration:

--conf spark.executor.instances=8

This distributes the workload more effectively across cluster nodes and reduces idle time for pending tasks.

Why the other options are incorrect:

A: Extending timeouts hides the symptom, not the root cause (lack of executors).

B: More memory per executor won't fix scheduling bottlenecks.

C: Reducing partition size may increase overhead and does not fix resource imbalance.

Databricks Exam Guide (June 2025): Section ''Troubleshooting and Tuning Apache Spark DataFrame API Applications'' --- tuning executors and cluster utilization.

Spark Configuration --- executor instances and resource scaling.

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The Spark configuration spark.executor.cores defines how many concurrent tasks can be executed within a single executor process.

Each executor is assigned a number of CPU cores.

Each core executes one task at a time.

Therefore, increasing spark.executor.cores allows an executor to run more tasks concurrently.

Example:

--conf spark.executor.cores=4

Each executor can run 4 parallel tasks.

Why the other options are incorrect:

B (spark.task.maxFailures): Sets retry attempts for failed tasks.

C (spark.executor.memory): Sets executor memory, not concurrency.

D (spark.sql.shuffle.partitions): Defines number of shuffle partitions, not executor concurrency.

Spark Configuration Guide --- Executor cores, tasks, and parallelism.

Databricks Exam Guide (June 2025): Section ''Apache Spark Architecture and Components'' --- executor configuration, CPU cores, and parallel task execution.