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

1. A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A) Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
B) Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges
C) Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
D) Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy

2. A data engineer needs to write a DataFramedfto a Parquet file, partitioned by the columncountry, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("append").partitionBy("country").parquet("/data/output")
B) df.write.mode("overwrite").parquet("/data/output")
C) df.write.partitionBy("country").parquet("/data/output")
D) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")

3. 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

4. A Spark application is experiencing performance issues in client mode because the driver is resource- constrained.
How should this issue be resolved?

A) Switch the deployment mode to local mode
B) Add more executor instances to the cluster
C) Switch the deployment mode to cluster mode
D) Increase the driver memory on the client machine

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

Solutions: