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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q97-Q102):NEW QUESTION # 97
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. Enhanced executor performance due to reduced log size
  • B. Improved job execution speed due to reduced logging overhead
  • C. Loss of access to past job logs and reduced debugging capability for completed jobs
  • D. Prevention of driver log accumulation during long-running jobs
Answer: C
Explanation:
The Spark History Server provides a web UI for viewing past completed applications, including event logs, stages, and performance metrics.
If disabled:
Spark jobs still run normally,
But users lose the ability to review historical job metrics, DAGs, or logs after completion.
Thus, debugging, performance analysis, and audit capabilities are lost.
Why the other options are incorrect:
A: Disabling History Server doesn't manage logs.
B/D: Minimal overhead; disabling doesn't improve runtime speed or executor performance.
Reference:
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - Spark UI, History Server, and event logging.
Spark Administration Docs - History Server functionality and configuration.

NEW QUESTION # 98
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?
  • A. spark.conf.set("spark.pandas.arrow.enabled", "true")
  • B. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
  • C. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
  • D. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
Answer: C
Explanation:
Apache Arrow is used under the hood to optimize conversion between Pandas and PySpark DataFrames. The correct configuration setting is:
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
From the official documentation:
"This configuration must be enabled to allow for vectorized execution and efficient conversion between Pandas and PySpark using Arrow." Option B is correct.
Options A, C, and D are invalid config keys and not recognized by Spark.
Final answer: B

NEW QUESTION # 99
A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing agroupByoperation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

D)

  • A. Use a regular Spark UDF:
    from pyspark.sql.functions import mean
    df.groupBy("user_id").agg(mean("value")).show()
  • B. Use themapInPandasAPI:
    df.mapInPandas(mean_func, schema="user_id long, value double").show()
  • C. Use theapplyInPandasAPI:
    df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
  • D. Use a Pandas UDF:
    @pandas_udf("double")
    def mean_func(value: pd.Series) -> float:
    return value.mean()
    df.groupby("user_id").agg(mean_func(df["value"])).show()
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct approach to perform a parallelizedgroupByoperation across Spark worker nodes using Pandas API is viaapplyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.
As per the Databricks documentation:
"applyInPandas()allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution." Option A is correct and achieves this parallel execution.
Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.
Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.
Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.
Therefore, to run agroupBywith parallel Pandas logic on Spark workers, Option A usingapplyInPandasis the only correct answer.
Reference: Apache Spark 3.5 Documentation # Pandas API on Spark # Grouped Map Pandas UDFs (applyInPandas)

NEW QUESTION # 100
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. Use an RDD action like reduce() to compute the maximum time
  • B. Configure the Spark UI to automatically collect maximum times
  • C. Use an accumulator to record the maximum time on the driver
  • D. Broadcast a variable to share the maximum time among workers
Answer: A
Explanation:
The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such as reduce() or aggregate().
From the documentation:
"To perform global aggregations on distributed data, actions like reduce() are commonly used to collect summaries such as min/max/avg." Accumulators (Option B) do not support max operations directly and are not intended for such analytics.
Broadcast (Option C) is used to send data to workers, not collect from them.
Spark UI (Option D) is a monitoring tool - not an analytics collection interface.
Final answer: A

NEW QUESTION # 101
A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with acollect()action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?
  • A. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
  • B. Thecollect()action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
  • C. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
  • D. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, the execution hierarchy is structured as follows:
Application: The highest-level unit, representing the user program built on Spark.
Job: Triggered by an action (e.g.,collect(),count()). Each action corresponds to a job.
Stage: A job is divided into stages based on shuffle boundaries. Each stage contains tasks that can be executed in parallel.
Task: The smallest unit of work, representing a single operation applied to a partition of the data.
When thecollect()action is invoked, Spark initiates a job. This job is then divided into stages at points where data shuffling is required (i.e., wide transformations). Each stage comprises tasks that are distributed across the cluster's executors, operating on individual data partitions.
This hierarchical execution model allows Spark to efficiently process large-scale data by parallelizing tasks and optimizing resource utilization.

NEW QUESTION # 102
......
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