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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

24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A. spark.sql("SELECT * FROM events.parquet").show()
B. spark.sql("SELECT schema FROM events.parquet").show()
C. spark.read.parquet("events.parquet").printSchema()
D. spark.read.format("parquet").load("events.parquet").show()

Question 2

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. 'header' 'false'
C. 'sep' ','
D. 'header' 'true'

Question 3

A data analyst wants to add a column date derived from a timestamp column.
Options:

A. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
B. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
C. dates_df.withColumn("date", f.to_date("timestamp")).show()
D. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()

Question 4

What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A. A job contains multiple tasks, and each task contains multiple stages.
B. A job contains multiple stages, and each stage contains multiple tasks.
C. A stage contains multiple tasks, and each task contains multiple jobs.
D. A stage contains multiple jobs, and each job contains multiple tasks.

Question 5

A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A. df.select("Name").distinct()
B. df.select("Name").orderBy(df["Name"].asc())
C. df.select("Name").distinct().orderBy(df["Name"].desc())
D. df.select("Name").distinct().orderBy(df["Name"])

Solutions: