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

1. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

A) It removes duplicates that arrive within the 30-minute window specified by the watermark.
B) It is not able to handle deduplication in this scenario.
C) It accepts watermarks in seconds and the code results in an error.
D) It removes all duplicates regardless of when they arrive.

2. What is the benefit of using Pandas on Spark for data transformations?
Options:

A) It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
B) It computes results immediately using eager execution, making it simple to use.
C) It is available only with Python, thereby reducing the learning curve.
D) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

3. 42 of 55.
A developer needs to write the output of a complex chain of Spark transformations to a Parquet table called events.liveLatest.
Consumers of this table query it frequently with filters on both year and month of the event_ts column (a timestamp).
The current code:
from pyspark.sql import functions as F
final = df.withColumn("event_year", F.year("event_ts")) \
.withColumn("event_month", F.month("event_ts")) \
.bucketBy(42, ["event_year", "event_month"]) \
.saveAsTable("events.liveLatest")
However, consumers report poor query performance.
Which change will enable efficient querying by year and month?

A) Change the bucket count (42) to a lower number
B) Add .sortBy() after .bucketBy()
C) Replace .bucketBy() with .partitionBy("event_year", "event_month")
D) Replace .bucketBy() with .partitionBy("event_year") only

4. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Shuffle join because no broadcast hints were provided
B) Shuffle join, because AQE is not enabled, and Spark uses a static query plan
C) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
D) Broadcast join, as df2 is smaller than the default broadcast threshold

5. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) Only actions trigger the execution of the transformation pipeline.
B) Transformations are executed immediately to build the lineage graph.
C) The Spark engine requires manual intervention to start executing transformations.
D) The Spark engine optimizes the execution plan during the transformations, causing delays.
E) Transformations are evaluated lazily.

Solutions: