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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

1. A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A) DataFrame.groupBy().agg()
B) DataFrame.withColumn()
C) DataFrame.filter()
D) DataFrame.select()

2. A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A) Spark SQL, Pandas API on Spark, and Structured Streaming
B) Spark DataFrames, Spark SQL, and MLlib
C) Spark DataFrames, Structured Streaming, and GraphX
D) Spark Streaming, GraphX, and Pandas API on Spark

3. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the repartition() transformation with a lower number of partitions
B) Use the distinct() transformation to combine similar partitions
C) Use the coalesce() transformation with a lower number of partitions
D) Use the sortBy() transformation to reorganize the data

4. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.to_date("timestamp")).show()
B) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
C) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
D) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()

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

Solutions: