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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 Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A) Configure the Spark UI to automatically collect maximum times
B) Use an accumulator to record the maximum time on the driver
C) Use an RDD action like reduce() to compute the maximum time
D) Broadcast a variable to share the maximum time among workers

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

3. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The operation will load all data into the driver's memory, potentially causing memory overflow
B) Data will be lost during conversion
C) The conversion will automatically distribute the data across worker nodes
D) The operation will fail if the Pandas DataFrame exceeds 1000 rows

4. 27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?

A) df = au_df.unionByName(nz_df, allowMissingColumns=False)
B) df = au_df.unionByName(nz_df, allowMissingColumns=True)
C) df = au_df.unionAll(nz_df)
D) df = au_df.union(nz_df, allowMissingColumns=True)

5. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions to a fixed value, such as 200
B) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
C) Set the number of partitions equal to the number of nodes in the cluster
D) Set the number of partitions equal to the total number of CPUs in the cluster

Solutions: