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Comprehensive and Detailed Explanation From Exact Extract:

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

Comprehensive and Detailed Explanation From Exact Extract:

In PySpark, when performing a join between two DataFrames, the columns do not have to share the same name. You can explicitly provide a join condition by comparing specific columns from each DataFrame.

This syntax is correct and fully supported:

df1.join(df2, df1.employee_id == df2.emp_id, how='inner')

This will perform an inner join betweendf1anddf2using theemployee_idfromdf1andemp_idfromdf2.

ThepartitionBy()method in Spark organizes output into subdirectories based on unique combinations of the specified columns:

e.g.

/path/to/output/color=red/fruit=apple/part-0000.parquet

/path/to/output/color=green/fruit=banana/part-0001.parquet

This improves query performance via partition pruning.

It does not consolidate into a single file.

Null values are allowed in partitions.

It does not "append" unless.mode("append")is used.

spark.executor.cores determines how many concurrent tasks an executor can run.

For example, if set to 4, each executor can run up to 4 tasks in parallel.

Other settings:

spark.task.maxFailures controls task retry logic.

spark.driver.cores is for the driver, not executors.

spark.executor.memory sets memory limits, not task concurrency.

The.mode("overwrite")ensures that existing files at the path will be replaced.

partitionBy("country")optimizes queries by writing data into partitioned folders.

Correct syntax:

df.write.mode("overwrite").partitionBy("country").parquet("/data/output")

— Source:Spark SQL, DataFrames and Datasets Guide