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

1. A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:

A) Add the option wholetext=False to the text() function
B) Add the option wholetext=True to the text() function
C) Add the option lineSep=", " to the text() function
D) Add the option lineSep='\n' to the text() function

2. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
B) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
C) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
D) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.

3. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the filter transformation is applied
B) When the groupBy transformation is applied
C) When the count action is applied
D) When the show action is applied

4. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

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

5. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Locate the executor logs on the Spark master node, typically under the /tmp directory.
B) Fetch the logs by running a Spark job with the spark-sql CLI tool.
C) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
D) Use the command spark-submit with the -verbose flag to print the logs to the console.

Solutions: