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

1. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Increase the size of the dataset to create more partitions
B) Increase the value of spark.sql.shuffle.partitions
C) Reduce the value of spark.sql.shuffle.partitions
D) Enable dynamic resource allocation to scale resources as needed

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

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

4. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) [Row(name='bambi'), Row(name='alladin', age=20)]
B) The code throws an error due to a schema mismatch.
C) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
D) [Row(name='alladin', age=20)]

5. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
B) Uses trigger() - default micro-batch trigger without interval.
C) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
D) Uses trigger(continuous='5 seconds') - continuous processing mode.

Solutions: