Databricks Certification 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:

Question #1
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A. psdf.to_dataframe()
B. psdf.to_pandas()
C. psdf.to_spark()
D. psdf.to_pyspark()

Question #2
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 wholetext=False to the text() function.
B. Add the option wholetext to the text() function.
C. Add the option lineSep=", " to the text() function.
D. Add the option lineSep to the text() function.

Question #3
A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A. It accepts watermarks in seconds and the code results in an error
B. It removes duplicates that arrive within the 30-minute window specified by the watermark
C. It is not able to handle deduplication in this scenario
D. It removes all duplicates regardless of when they arrive

Question #4
41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A. df1.orderBy("Name", "count")
B. df1.orderBy(col("count").desc(), col("Name").asc())
C. df1.sort("Name", "count")
D. df1.orderBy(col("Name").desc(), col("count").asc())

Question #5
A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A. Optimizing the layout of Delta files on disk
B. Collecting persistent table statistics and storing them in the metastore for future use
C. Dynamically switching join strategies
D. Improving the performance of single-stage Spark jobs

Solutions: