Online Access Free Associate-Developer-Apache-Spark-3.5 Practice Test

A data engineer is working on the DataFrame:

(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?

• A.df.select("Name").distinct()
• B.df.select("Name").distinct().orderBy(df["Name"])
• C.df.select("Name").orderBy(df["Name"].asc())
• D.df.select("Name").distinct().orderBy(df["Name"].desc())

Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

• A.20
• B.Same number as the cluster executors
• C.1
• D.10

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_spark()
• D.psdf.to_pandas()

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

What is the benefit of using Pandas on Spark for data transformations?
Options:

• A.It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
• B.It computes results immediately using eager execution, making it simple to use.
• C.It is available only with Python, thereby reducing the learning curve.
• D.It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost- efficient.