count_if(condition) counts the number of rows that meet the specified boolean condition.
In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-43773)." Why other options are incorrect or outdated:
A uses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
C performs a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
D incorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore, B is the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.
Explanation:
The correct answer is B because it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.

f.to_date() converts a timestamp or string to a DateType.
Ideal for extracting the date component (year-month-day) from a full timestamp.
Example:
from pyspark.sql.functions import to_date
dates_df.withColumn("date", to_date("timestamp"))

To filter rows based on a condition and display them in Spark, use filter(...).show():
employees_df.filter(employees_df.tenure >= 5).show()
Option A is correct and shows the results.
Option B filters but doesn't display them.
Option C uses Python's built-in filter, not Spark.
Option D collects the results to the driver, which is unnecessary if .show() is sufficient.
Final answer: A

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.
Reference:
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.

In Spark's client mode, the driver runs on the local machine that submitted the job. If that machine is resource-constrained (e.g., low memory), performance degrades.
From the Spark documentation:
"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability." Option A is incorrect - executors do not help the driver directly.
Option B might help short-term but does not scale.
Option C is correct - switching to cluster mode moves the driver to the cluster.
Option D (local mode) is for development/testing, not production.
Final answer: C