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

1. 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.json("path/to/file", overwrite=True)
C) df.write.overwrite.json("path/to/file")
D) df.write.mode("overwrite").json("path/to/file")

2. 14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A) It stores all data in a single Parquet file.
B) It throws an error if there are null values in either partition column.
C) It creates separate directories for each unique combination of color and taste.
D) It appends new partitions to an existing Parquet file.

3. 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 increases the partition size for df1 and df2.
C) It filters the id values before performing the join.
D) It ensures that the join happens only when the id values are identical.

4. A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A) .trigger(processingTime='1 second')
B) .trigger(continuous='1 second')
C) .trigger(availableNow=True)
D) .trigger(continuous=True)

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

Solutions: