Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to add a new column with a case-when expression (fix the error) df = df.withColumn("category", F.when(df["price"] ] 100, "Expensive").otherwise("Cheap")) df.show() What is the error in this code, and how can it be fixed?

• A.The error is missing parentheses around the conditions in the when function. Fix: F.when((df["price"] ] 100), "Expensive").otherwise("Cheap")
• B.The error is using the wrong syntax for case-when expressions. Fix: Use SQL-like syntax with CASE WHEN and END.
• C.The error is attempting to modify the original DataFrame in-place. Fix: Use df.withColumn to create a new DataFrame with the added column.
• D.There is no error in the code snippet.

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What are the potential trade-offs to consider when using checkpointing in Spark applications?

• A.Checkpointing always improves performance and has no drawbacks
• B.Checkpointing introduces overhead for storing and recovering data, impacting performance
• C.Checkpointing requires manual configuration and can be error-prone
• D.All of the above

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You notice degraded read performance on an Iceberg table after many updates and deletes. What maintenance task should you perform to improve this?

• A.Rewrite manifest files
• B.Compact data files
• C.Delete old snapshots
• D.Rebuild the Iceberg metadata table

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Which of the following strategies would NOT be recommended for managing skewed data during join operations in Spark?

• A.Salting the keys to distribute the data more evenly across partitions.
• B.Using a broadcast join assuming one dataset is small enough to fit into memory.
• C.Applying a filter to remove outliers that cause data skewness before joining.
• D.Increasing the number of partitions to distribute the skewed data more evenly.

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You need to create a new Hive table from a Spark DataFrame. What are the different approaches you can consider?

• A.Directly write the DataFrame to a directory in HDFS and define a corresponding Hive table schema
• B.Use the DataFrame.write.saveAsTable("table_name") method with appropriate options
• C.Convert the DataFrame to a temporary table and then use HiveQL commands to create a permanent table
• D.All of the above

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