Databricks Associate-Developer-Apache-Spark-3.5

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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. A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:

import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_256() function be changed to in order to fix this error?

A) def shake_256(df: pd.Series) -> pd.Series:
B) def shake_256(raw: str) -> str:
C) def shake_256(df: pd.Series) -> str:
D) def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:

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

3. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) COMPLETE
B) APPEND
C) REPLACE
D) AGGREGATE

4. What is the benefit of Adaptive Query Execution (AQE)?

A) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
B) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
C) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
D) It allows Spark to optimize the query plan before execution but does not adapt during runtime.

5. A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:

A) Add the option wholetext=False to the text() function
B) Add the option lineSep=", " to the text() function
C) Add the option lineSep='\n' to the text() function
D) Add the option wholetext=True to the text() function

Solutions: