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

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

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

3. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
B) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
C) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
D) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.

4. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write(path="/some/path").saveAsTable("default_table")
B) users.write.saveAsTable("default_table").option("path", "/some/path")
C) users.write.option("path", "/some/path").saveAsTable("default_table")
D) users.write.saveAsTable("default_table", path="/some/path")

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

Solutions: