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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q10-Q15):

NEW QUESTION # 10
11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A. spark.executor.cores
B. spark.sql.shuffle.partitions
C. spark.executor.memory
D. spark.task.maxFailures

Answer: A

Explanation:
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.

NEW QUESTION # 11
49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream
.format("console")
.outputMode("???")
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A. AGGREGATE
B. COMPLETE
C. REPLACE
D. APPEND

Answer: B

Explanation:
Structured Streaming supports three output modes:
Append: Writes only new rows since the last trigger.
Update: Writes only updated rows.
Complete: Writes the entire result table after every trigger execution.
For aggregations like groupBy().count(), only complete mode outputs the entire table each time.
Example:
aggDF.writeStream
.outputMode("complete")
.format("console")
.start()
Why the other options are incorrect:
A: "AGGREGATE" is not a valid output mode.
C: "REPLACE" does not exist.
D: "APPEND" writes only new rows, not the full table.
Reference:
PySpark Structured Streaming - Output Modes (append, update, complete).
Databricks Exam Guide (June 2025): Section "Structured Streaming" - output modes and use cases for aggregations.

NEW QUESTION # 12
Which command overwrites an existing JSON file when writing a DataFrame?

A. df.write.json("path/to/file", overwrite=True)
B. df.write.overwrite.json("path/to/file")
C. df.write.mode("overwrite").json("path/to/file")
D. df.write.format("json").save("path/to/file", mode="overwrite")

Answer: C

Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
Reference:PySpark DataFrameWriter API

NEW QUESTION # 13
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A. regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
B. regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
C. regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
D. regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)

Answer: C

Explanation:
The question requires creating a dictionary where keys are region values and values are the corresponding region_id integers. Furthermore, it asks to retrieve only the smallest 3 region_id values.
Key observations:
.select('region', 'region_id') puts the column order as expected by dict() - where the first column becomes the key and the second the value.
.sort('region_id') ensures sorting in ascending order so the smallest IDs are first.
.take(3) retrieves exactly 3 rows.
Wrapping the result in dict(...) correctly builds the required Python dictionary: { 'AFRICA': 0, 'AMERICA': 1, 'ASIA': 2 }.
Incorrect options:
Option B flips the order to region_id first, resulting in a dictionary with integer keys - not what's asked.
Option C uses .limit(3) without sorting, which leads to non-deterministic rows based on partition layout.
Option D sorts in descending order, giving the largest rather than smallest region_ids.
Hence, Option A meets all the requirements precisely.

NEW QUESTION # 14
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
B. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame

Answer: B

Explanation:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.

NEW QUESTION # 15
......

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