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

1. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

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

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

3. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
B) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
C) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
D) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")

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

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

Solutions: