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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

15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A. streaming_df.show()
B. streaming_df.filter("count < 30")
C. streaming_df.select(countDistinct("name"))
D. streaming_df.count()

Question 2

A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A. .format("parquet")
.option("path", "path/to/destination/dir")
B. CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
C. .option("format", "parquet")
.option("location", "path/to/destination/dir")
D. .format("parquet")
.option("location", "path/to/destination/dir")

Question 3

A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
B. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
C. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
D. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))

Question 4

A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A. Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
B. Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy
C. Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
D. Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges

Question 5

An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

A. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
B. The code fails to execute because it must use on='employee_id' to specify the join column explicitly
C. The code fails to execute because PySpark does not support joining DataFrames with a different structure
D. The code fails to execute because the column names employee_id and emp_id do not match automatically

Solutions: