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

1. A developer runs:

What is the result?
Options:

A) It stores all data in a single Parquet file.
B) It creates separate directories for each unique combination of color and fruit.
C) It appends new partitions to an existing Parquet file.
D) It throws an error if there are null values in either partition column.

2. A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/*")
C) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
D) df = spark.read.parquet("/path/events/data/")

3. A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

4. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger() - default micro-batch trigger without interval.
B) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
C) Uses trigger(continuous='5 seconds') - continuous processing mode.
D) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

5. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Reduce the size of the data partitions to improve task scheduling.
B) Increase the number of executor instances to handle more concurrent tasks.
C) Set the spark.network.timeout property to allow tasks more time to complete without being killed.
D) Increase the executor memory allocation in the Spark configuration.

Solutions: