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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 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'))

2. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
B) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
C) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
D) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)

3. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
B) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
C) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
D) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.

4. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

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

5. 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())

Solutions: