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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. A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) select
B) filter
C) coalesce
D) groupBy

2. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
B) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
C) Use spark.read.json() with the inferSchema option set to true
D) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.

3. 35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

A) By configuring the option checkpointLocation during writeStream.
B) By configuring the option recoveryLocation during SparkSession initialization.
C) By configuring the option checkpointLocation during readStream.
D) By configuring the option recoveryLocation during writeStream.

4. 7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

A) 'header' 'false'
B) 'sep' ','
C) 'sep' '|'
D) 'header' 'true'

5. A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A) Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
B) Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
C) Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
D) Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()

Solutions: