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

1. 1 of 55. A data scientist wants to ingest a directory full of plain text files so that each record in the output DataFrame contains the entire contents of a single file and the full path of the file the text was read from.
The first attempt does read the text files, but each record contains a single line. This code is shown below:
txt_path = "/datasets/raw_txt/*"
df = spark.read.text(txt_path) # one row per line by default
df = df.withColumn("file_path", input_file_name()) # add full path
Which code change can be implemented in a DataFrame that meets the data scientist's requirements?

A) Add the option lineSep=", " to the text() function.
B) Add the option lineSep to the text() function.
C) Add the option wholetext to the text() function.
D) Add the option wholetext=False to the text() function.

2. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option checkpointLocation during readStream
B) By configuring the option recoveryLocation during writeStream
C) By configuring the option checkpointLocation during writeStream
D) By configuring the option recoveryLocation during the SparkSession initialization

3. A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
B) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
C) df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")
D) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

4. 40 of 55.
A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5.
The original code:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?

A) result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
B) result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count()
C) result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()
D) result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))

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 \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

Solutions: