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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. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Broadcast join, as df2 is smaller than the default broadcast threshold
B) Shuffle join, because AQE is not enabled, and Spark uses a static query plan
C) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
D) Shuffle join because no broadcast hints were provided

2. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.format("json").save("path/to/file", mode="overwrite")
B) df.write.mode("overwrite").json("path/to/file")
C) df.write.overwrite.json("path/to/file")
D) df.write.json("path/to/file", overwrite=True)

3. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
B) regions_dict = dict(regions.take(3))
C) regions_dict = regions.select("region_id", "region_name").take(3)
D) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())

4. A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?

A) employees_df.filter(employees_df.tenure >= 5).collect()
B) filter(employees_df.tenure >= 5)
C) employees_df.where(employees_df.tenure >= 5)
D) employees_df.filter(employees_df.tenure >= 5).show()

5. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Reduce the value of spark.sql.shuffle.partitions
B) Increase the size of the dataset to create more partitions
C) Enable dynamic resource allocation to scale resources as needed
D) Increase the value of spark.sql.shuffle.partitions

Solutions: