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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. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_dataframe()
B) psdf.to_pandas()
C) psdf.to_spark()
D) psdf.to_pyspark()

2. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It filters the id values before performing the join.
C) It increases the partition size for df1 and df2.
D) It ensures that the join happens only when the id values are identical.

3. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
B) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
C) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
D) spark.read.orc("/file/test_data.orc").selected("col1", "col2")

4. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.sql("SELECT schema FROM events.parquet").show()
B) spark.read.parquet("events.parquet").printSchema()
C) spark.sql("SELECT * FROM events.parquet").show()
D) spark.read.format("parquet").load("events.parquet").show()

5. 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 \
.sort("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 \
.sortWithinPartitions("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")

Solutions: