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

1. 7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

A) 'sep' '|'
B) 'sep' ','
C) 'header' 'false'
D) 'header' 'true'

2. 19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
What should the signature of the shake_256() function be changed to in order to fix this error?

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

3. What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be removed from the start date
B) An error message of an invalid parameter will be returned
C) The same start date will be returned
D) The number of days specified will be added to the start date

4. A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.partitionBy("country").parquet("/data/output")
B) df.write.mode("append").partitionBy("country").parquet("/data/output")
C) df.write.mode("overwrite").parquet("/data/output")
D) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")

5. Given this code:

.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A) The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
B) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
C) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
D) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.

Solutions: