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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. 54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
B) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
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.

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'

3. 18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It filters the id values before performing the join.
C) It increases the partition size for df1 and df2.
D) It ensures that the join happens only when the id values are identical.

4. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A) streaming_df.groupby("Id") .count ()
B) streaming_df.filter (col("count") < 30).show()
C) streaming_df. select (countDistinct ("Name") )
D) streaming_df.orderBy("timestamp").limit(4)

5. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It filters the id values before performing the join.
C) It increases the partition size for df1 and df2.
D) It ensures that the join happens only when the id values are identical.

Solutions: