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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. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the filter transformation is applied
B) When the groupBy transformation is applied
C) When the count action is applied
D) When the show action is applied

2. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
B) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
C) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
D) A new SparkSession is created every time the getOrCreate() method is invoked.

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('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'), 'left')

4. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_pyspark()
B) psdf.to_dataframe()
C) psdf.to_pandas()
D) psdf.to_spark()

5. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
B) [Row(name='alladin', age=20)]
C) The code throws an error due to a schema mismatch.
D) [Row(name='bambi'), Row(name='alladin', age=20)]

Solutions: