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

1. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.read.parquet("events.parquet").printSchema()
B) spark.read.format("parquet").load("events.parquet").show()
C) spark.sql("SELECT schema FROM events.parquet").show()
D) spark.sql("SELECT * FROM events.parquet").show()

2. A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)

B)

C)

D)

A) exploded_df = exploded_df.select("record_datetime", "record_exploded")
B) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
C) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
D) exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")

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

4. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

A) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')

5. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
B) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
C) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
D) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).

Solutions: