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

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

2. What is a feature of Spark Connect?

A) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B) It has built-in authentication
C) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
D) It supports only PySpark applications

3. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A) A job contains multiple stages, and each stage contains multiple tasks.
B) A job contains multiple tasks, and each task contains multiple stages.
C) A stage contains multiple tasks, and each task contains multiple jobs.
D) A stage contains multiple jobs, and each job contains multiple tasks.

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

5. 12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A) filtered_users = raw_users.na.drop("all")
B) filtered_users = raw_users.dropna(how="any")
C) filtered_users = raw_users.na.drop("any")
D) filtered_users = raw_users.dropna(how="all")

Solutions: