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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 developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A sort-merge join
B) A Cartesian join
C) A broadcast nested loop join
D) A shuffled hash join

2. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

A) Improved job execution speed due to reduced logging overhead
B) Loss of access to past job logs and reduced debugging capability for completed jobs
C) Prevention of driver log accumulation during long-running jobs
D) Enhanced executor performance due to reduced log size

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

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

5. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
B) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
C) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
D) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

Solutions: