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

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

2. What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?

A) cache()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK) andpersist()- Can be used to set different storage levels to persist the contents of the DataFrame
B) persist()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) andcache()- Can be used to set different storage levels to persist the contents of the DataFrame.
C) Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.
D) Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)

3. A data engineer writes the following code to join two DataFramesdf1anddf2:
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) Broadcast join, as df2 is smaller than the default broadcast threshold
C) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
D) Shuffle join, because AQE is not enabled, and Spark uses a static query plan

4. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.mode("overwrite").json("path/to/file")
B) df.write.overwrite.json("path/to/file")
C) df.write.format("json").save("path/to/file", mode="overwrite")
D) df.write.json("path/to/file", overwrite=True)

5. What is a feature of Spark Connect?

A) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
C) It has built-in authentication
D) It supports only PySpark applications

Solutions: