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1. An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

A) The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
B) The code fails to execute because the column names employee_id and emp_id do not match automatically
C) The code fails to execute because PySpark does not support joining DataFrames with a different structure
D) The code fails to execute because it must use on='employee_id' to specify the join column explicitly

2. A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A) Spark Streaming, GraphX, and Pandas API on Spark
B) Spark SQL, Pandas API on Spark, and Structured Streaming
C) Spark DataFrames, Structured Streaming, and GraphX
D) Spark DataFrames, Spark SQL, and MLlib

3. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option checkpointLocation during readStream
B) By configuring the option recoveryLocation during the SparkSession initialization
C) By configuring the option recoveryLocation during writeStream
D) By configuring the option checkpointLocation during writeStream

4. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
B) Debuggability: the ability to perform interactive debugging directly from the application code
C) Stability: isolation of application code and dependencies from each other and the Spark driver
D) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself

5. What is the benefit of Adaptive Query Execution (AQE)?

A) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
B) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
D) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.

問題與答案：