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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. 27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?

A) df = au_df.unionByName(nz_df, allowMissingColumns=False)
B) df = au_df.unionAll(nz_df)
C) df = au_df.union(nz_df, allowMissingColumns=True)
D) df = au_df.unionByName(nz_df, allowMissingColumns=True)

2. 25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A) employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")
B) employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)
C) employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
D) employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")

3. Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A) Same number as the cluster executors
B) 20
C) 10
D) 1

4. 35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

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

5. 37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
B) customerDF = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
C) customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
D) customerDF = customerDF.select("email").alias("username", "domain")

Solutions: