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

1. A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A) query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()
B) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()

2. Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A) spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
B) spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
C) spark.conf.set("spark.sql.execution.arrow.enabled", "true")
D) spark.conf.set("spark.pandas.arrow.enabled", "true")

3. A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A)

B)

C)

D)

A) result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))
B) result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
C) result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
D) result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price"))

4. A developer is trying to join two tables,sales.purchases_fctandsales.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 thepurchases_fcttable that do not exist in thecustomer_dimtable 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 = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
B) fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
C) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
D) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')

5. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
C) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself

Solutions: