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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
B) Locate the executor logs on the Spark master node, typically under the/tmpdirectory.
C) Fetch the logs by running a Spark job with thespark-sqlCLI tool.
D) Use the commandspark-submitwith the-verboseflag to print the logs to the console.

2. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
B) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
C) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
D) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

3. A DataFramedfhas columnsname,age, andsalary. The developer needs to sort the DataFrame byagein ascending order andsalaryin descending order.
Which code snippet meets the requirement of the developer?

A) df.orderBy("age", "salary", ascending=[True, False]).show()
B) df.sort("age", "salary", ascending=[False, True]).show()
C) df.sort("age", "salary", ascending=[True, True]).show()
D) df.orderBy(col("age").asc(), col("salary").asc()).show()

4. Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A) It allows for remote execution of Spark jobs
B) It provides a way to run Spark applications remotely in any programming language
C) It is primarily used for data ingestion into Spark from external sources
D) It can be used to interact with any remote cluster using the REST API

5. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_pandas()
B) psdf.to_spark()
C) psdf.to_dataframe()
D) psdf.to_pyspark()

Solutions: