DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM PASS GUIDE | VALID ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM SYLLABUS

Databricks Associate-Developer-Apache-Spark-3.5 Exam Pass Guide | Valid Associate-Developer-Apache-Spark-3.5 Exam Syllabus

Databricks Associate-Developer-Apache-Spark-3.5 Exam Pass Guide | Valid Associate-Developer-Apache-Spark-3.5 Exam Syllabus

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q84-Q89):

NEW QUESTION # 84
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

  • A. A stage contains multiple jobs, and each job contains multiple tasks.
  • B. A job contains multiple tasks, and each task contains multiple stages.
  • C. A stage contains multiple tasks, and each task contains multiple jobs.
  • D. A job contains multiple stages, and each stage contains multiple tasks.

Answer: D

Explanation:
A Sparkjobis triggered by an action (e.g., count, show).
The job is broken intostages, typically one per shuffle boundary.
Eachstageis divided into multipletasks, which are distributed across worker nodes.
Reference:Spark Execution Model


NEW QUESTION # 85
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
frompyspark.sql.functionsimportbroadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

  • A. It ensures that the join happens only when the id values are identical.
  • B. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
  • C. It filters the id values before performing the join.
  • D. It increases the partition size for df1 and df2.

Answer: B

Explanation:
broadcast(df1) tells Spark to send the small DataFrame (df1) to all worker nodes.
This eliminates the need for shuffling df1 during the join.
Broadcast joins are optimized for scenarios with one large and one small table.
Reference:Spark SQL Performance Tuning Guide - Broadcast Joins


NEW QUESTION # 86
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. Only actions trigger the execution of the transformation pipeline.
  • B. The Spark engine optimizes the execution plan during the transformations, causing delays.
  • C. Transformations are executed immediately to build the lineage graph.
  • D. The Spark engine requires manual intervention to start executing transformations.
  • E. Transformations are evaluated lazily.

Answer: A,E

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 # 87
A data analyst builds a Spark application to analyze finance data and performs the following operations:filter, select,groupBy, andcoalesce.
Which operation results in a shuffle?

  • A. coalesce
  • B. select
  • C. filter
  • D. groupBy

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThegroupBy()operation causes a shuffle because it requires all values for a specific key to be brought together, which may involve moving data across partitions.
In contrast:
filter()andselect()are narrow transformations and do not cause shuffles.
coalesce()tries to reduce the number of partitions and avoids shuffling by moving data to fewer partitions without a full shuffle (unlikerepartition()).
Reference:Apache Spark - Understanding Shuffle


NEW QUESTION # 88
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(path="/some/path").saveAsTable("default_table")
  • B. users.write.saveAsTable("default_table").option("path", "/some/path")
  • C. users.write.saveAsTable("default_table", path="/some/path")
  • D. users.write.option("path", "/some/path").saveAsTable("default_table")

Answer: D

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 # 89
......

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