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

1. A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A) Modify the Spark configuration to disable garbage collection
B) Increase the memory allocated to the Spark Driver.
C) Cache large DataFrames to persist them in memory.
D) Optimize the data processing logic by repartitioning the DataFrame.

2. 37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF = customerDF.select("email").alias("username", "domain")
B) customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
C) customerDF = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
D) customerDF = customerDF.withColumn("domain", col("email").split("@")[1])

3. A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

4. A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
B) fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
C) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
D) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')

5. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions equal to the total number of CPUs in the cluster
B) Set the number of partitions equal to the number of nodes in the cluster
C) Set the number of partitions to a fixed value, such as 200
D) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB

Solutions: