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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q39-Q44):

NEW QUESTION # 39
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

  • A. The Spark engine requires manual intervention to start executing transformations.
  • B. Only actions trigger the execution of the transformation pipeline.
  • C. Transformations are executed immediately to build the lineage graph.
  • D. Transformations are evaluated lazily.
  • E. The Spark engine optimizes the execution plan during the transformations, causing delays.

Answer: B,D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.
g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.
Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.
This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.


NEW QUESTION # 40
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

  • A. Data will be lost during conversion
  • B. The operation will fail if the Pandas DataFrame exceeds 1000 rows
  • C. The conversion will automatically distribute the data across worker nodes
  • D. The operation will load all data into the driver's memory, potentially causing memory overflow

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D


NEW QUESTION # 41
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

  • A. Modify the Spark configuration to disable garbage collection
  • B. Cache large DataFrames to persist them in memory.
  • C. Optimize the data processing logic by repartitioning the DataFrame.
  • D. Increase the memory allocated to the Spark Driver.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot ofGC overhead limit exceedederrors in the driver logs, it's a sign that the driver is running out of memory."
-Spark Tuning Guide
Why others are incorrect:
Amay help but does not directly address the driver memory shortage.
Bis not a valid action; GC cannot be disabled.
Dincreases memory usage, worsening the problem.


NEW QUESTION # 42
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

  • A. users.write.option("path", "/some/path").saveAsTable("default_table")
  • B. users.write.saveAsTable("default_table", path="/some/path")
  • C. users.write(path="/some/path").saveAsTable("default_table")
  • D. users.write.saveAsTable("default_table").option("path", "/some/path")

Answer: A

Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table


NEW QUESTION # 43
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

  • A. A Cartesian join
  • B. A broadcast nested loop join
  • C. A sort-merge join
  • D. A shuffled hash join

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B


NEW QUESTION # 44
......

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