Free Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Practice Questions – 2025 Updated

In client deployment mode, the Spark driver program runs on the machine from which the application was submitted. If this client machine is resource-constrained (e.g., has limited memory or CPU), it becomes a performance bottleneck for the entire application, as the driver is responsible for creating the SparkContext, analyzing the query, and coordinating task execution.

Switching to cluster mode resolves this issue by launching the driver on a worker node within the cluster itself. These worker nodes are typically provisioned with significantly more resources than a client/gateway machine. This allows the driver to leverage the cluster's resources, eliminating the bottleneck and making the application more robust, as its lifecycle is no longer tied to the client machine.

A. Add more executor instances to the cluster: This would increase the number of tasks the driver needs to manage, likely exacerbating the resource constraint on the already-overloaded driver.

B. Increase the driver memory on the client machine: While this could alleviate the issue, it assumes the client machine has more resources to allocate and may not be the most scalable or robust solution.

D. Switch the deployment mode to local mode: This would run the entire application, including the driver and executors, on a single machine, which is intended for development and would not solve a resource issue in a distributed setting.

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1. Apache Spark 3.x Official Documentation

"Submitting Applications":

Section: "Deploy Mode"

Content: The documentation specifies that for client mode

"the driver is launched in the same process as the client that submits the application." In contrast

for cluster mode

"the driver is launched on one of the worker nodes inside the cluster... This is useful for jobs that are launched from a gateway machine that is far from the worker machines." This directly supports moving the driver to a more powerful

co-located machine within the cluster to resolve resource constraints.

2. Karau

H.

Konwinski

A.

Wendell

P.

& Zaharia

M. (2020). Learning Spark: Lightning-Fast Data Analytics (2nd ed.). O'Reilly Media.

Chapter 17: Deploying Apache Spark

Section: "The spark-submit Command":

Content: The authors (who include the original creators of Spark and Databricks employees) explain that cluster mode is preferred for production jobs. They state

"In cluster mode

the Spark driver runs on one of the worker nodes of the cluster... The driver program is now running on a machine that is on the same network as the worker nodes

so it can communicate with them quickly." This highlights the performance and resource advantages of running the driver within the cluster.

3. UC Berkeley

Foundations of Data Science (Data 100)

"Lecture 20: Spark":

Section: "Spark Architecture: Cluster Manager"

Content: University course materials often explain the trade-offs. For client mode

they note it's good for interactive development but can be a bottleneck if the driver requires significant resources. For cluster mode

they emphasize its suitability for production

as the driver runs on a dedicated machine within the cluster

managed by the cluster manager

providing better resource allocation and fault tolerance.

In Spark's Local Mode, the entire application—driver, master, and executor—runs within a single Java Virtual Machine (JVM) on a single machine. Parallelism is achieved through multi-threading. To ensure optimal resource utilization, specifically of the CPU, the developer should configure the number of threads Spark can use for task execution. By setting the master URL to local[k], where k is the number of threads, or more commonly local[], Spark is instructed to create as many worker threads as there are logical CPU cores on the machine. This maximizes parallelism and ensures the machine's processing capacity is fully utilized during testing.

A. Configuring the application for cluster mode is a different deployment strategy and does not optimize the existing Local Mode setup as requested.

C. Dynamic allocation is a feature for cluster managers (like YARN or Kubernetes) to add or remove executors; it is not applicable to Local Mode, which runs in a single JVM.

D. While memory is important, spark.executor.memory is less relevant in Local Mode where everything shares the driver's memory. The primary lever for CPU utilization is the thread count, not memory allocation.

1. Apache Spark 3.5.1 Documentation

Submitting Applications

Master URLs: The documentation explicitly states for Local Mode: "local[K]: Run Spark locally with K worker threads (ideally

set this to the number of cores on your machine)." and "local[] Run Spark locally with as many worker threads as logical cores on your machine." This directly supports configuring the number of threads based on CPU cores for optimal use.

2. Apache Spark 3.5.1 Documentation

Configuration

Dynamic Allocation: This section details the spark.dynamicAllocation.enabled property and specifies its use with Spark Standalone mode

YARN mode

and Kubernetes mode. It makes no mention of Local Mode

confirming the feature is for cluster environments.

3. Karau

H.

Konwinski

A.

Wendell

P.

& Zaharia

M. (2015). Learning Spark: Lightning-Fast Big Data Analysis. O'Reilly Media

Inc. Chapter 4

"Deploying Spark": This foundational text explains that in Local Mode

"you can specify the number of threads to use by passing a number in brackets (for example

local[4])." It further clarifies that using local[] is a common practice to use one thread per core

which is the standard approach for maximizing local resource utilization.

The Iterator[Series] -> Iterator[Series] Pandas UDF is the ideal pattern for this scenario. This type of UDF is invoked once for each data partition. The function receives an iterator of pandas Series, allowing the expensive model (gettranslationmodel) to be loaded just once at the beginning of the function. The code can then iterate through the batches of data within the partition, applying the already-loaded model to each batch. This avoids the significant performance overhead of re-initializing the model for every single batch, which is what happens with a standard Series -> Series UDF.

A. Converting to a standard PySpark UDF would be less performant, as it operates row-by-row, potentially loading the model for every single row instead of for each batch.

B. A Series -> Scalar UDF still processes data in batches (pd.Series), so it would not solve the underlying problem of reloading the model for each batch.

C. While mapInPandas also uses an iterator pattern, the most direct and idiomatic way to modify an existing Pandas UDF for this optimization is to change its type to Iterator[Series] -> Iterator[Series].

1. Apache Spark Documentation

Pandas UDFs (a.k.a. Vectorized UDFs): The section on "Iterator of Series to Iterator of Series UDF" explicitly states: "It is useful when the UDF execution requires an expensive initialization... The example below shows how to use an iterator of series UDF to load a large model once and then apply it to each series in the partition." This directly addresses the question's scenario.

Source: Apache Spark 3.5.0 Documentation

pyspark.sql.functions.pandasudf

Section: "Iterator of Series to Iterator of Series UDF".

2. Databricks Documentation

User-defined functions - Python: The documentation describes the Iterator[pd.Series] -> Iterator[pd.Series] UDF and highlights its primary use case: "This is useful when the UDF execution requires an expensive state to be initialized... The state can be created once and used for the entire partition."

Source: Databricks Documentation

"User-defined functions - Python"

Section: "Pandas UDFs (vectorized UDFs)"

Subsection: "Iterator of Series to Iterator of Series".

To connect an existing Spark application to a Spark Connect server without code modification, the connection must be configured externally. The two primary methods are using a command-line flag when launching the application (like the PySpark shell) or setting an environment variable. The --remote "sc://localhost" flag instructs the PySpark shell to operate in Connect mode and connect to the specified server. Similarly, setting the SPARKREMOTE="sc://localhost" environment variable before launching any Spark application (including a shell or a submitted job) will configure the default SparkSession to connect to the specified remote server, overriding the default behavior of creating a local Spark session.

A. The https:// protocol is incorrect for Spark Connect; the correct protocol is sc://.

D. This option requires modifying the application's source code by adding .remote() to the SparkSession.builder call, which the question explicitly prohibits.

E. spark.connect.grpc.binding.port is a server-side configuration property that specifies the port the Spark Connect server listens on, not how a client connects to it.

1. Apache Spark 3.5.1 Documentation

Spark Connect Overview

"Start Spark Connect server and client": This section explicitly demonstrates the two valid methods.

For the command-line option: "To use Spark Connect with the PySpark shell

run the following command in the Spark root directory: ./bin/pyspark --remote sc://localhost". This validates option B.

For the environment variable: "Alternatively

you can set the SPARKREMOTE environment variable

for example

export SPARKREMOTE="sc://localhost"". This validates option C.

The documentation also shows the code-based method

.remote("sc://localhost")

confirming why option D is a valid method in general but incorrect for this specific question's constraint.

The requirement is to remove any record that contains a null value in any of its fields. The pyspark.sql.DataFrameNaFunctions.drop() method is used for this purpose. The how parameter controls the dropping logic. Setting how='any' instructs Spark to drop a row if it contains one or more null values across any of its columns. This directly fulfills the data scientist's requirement. The default behavior of df.na.drop() is also equivalent to how='any'.

A. filtereddf = usersrawdf.na.drop(thresh=0): The thresh parameter keeps rows with at least that many non-null values. thresh=0 would keep all rows, as every row has at least zero non-nulls.

B. filtereddf = usersrawdf.na.drop(how='all'): This option drops a row only if all of its values are null, which is too restrictive and does not meet the requirement to drop rows with any null value.

D. filtereddf = usersrawdf.na.drop(how='all', thresh=None): This is functionally identical to option B. Specifying thresh=None is redundant as it's the default and does not alter the incorrect how='all' logic.

1. Apache Spark Official Documentation: The pyspark.sql.DataFrameNaFunctions.drop documentation explicitly defines the how parameter.

Source: Apache Spark 3.5.1 Documentation

pyspark.sql.DataFrameNaFunctions.drop.

Reference: "how – str

optional – 'any' or 'all'. If 'any'

drop a row if it contains any nulls. If 'all'

drop a row only if all its values are null."

2. Databricks Official Documentation: The Databricks guide on data cleaning confirms this behavior.

Source: Databricks Documentation

"Handle missing data".

Reference: In the section on dropping rows

it states: "You can drop rows with any null or NaN values using df.na.drop() or df.dropna(). This is the default." It further clarifies that df.na.drop("any") is the explicit form.

3. University Courseware: Reputable university courses on data science with Spark teach this fundamental operation.

Source: University of California

Berkeley

Data 100: Principles and Techniques of Data Science.

Reference: Course materials on data cleaning with PySpark demonstrate that df.dropna(how='any') is the standard method to remove rows containing any missing values. The dropna() alias is interchangeable with na.drop().

Apache Spark's execution model is fundamentally based on lazy evaluation. Transformations, such as select(), filter(), or groupBy(), do not execute immediately. Instead, Spark builds a Directed Acyclic Graph (DAG) representing the logical execution plan. This plan outlines the sequence of operations to be performed. The actual computation and data processing are deferred until an action is invoked. Actions, like count(), collect(), or save(), are operations that trigger the execution of the entire pipeline of transformations defined in the DAG to produce a final result, either by returning it to the driver or writing it to storage.

A. Transformations are not executed immediately; they are lazy and only build the computation plan (DAG).

B. Optimization occurs when an action is called, not during the definition of transformations. The delay is due to lazy evaluation, not the optimization process itself.

D. The trigger for execution is a programmatic "action" called in the code, not a form of "manual intervention" by a user.

1. Apache Spark Official Documentation

RDD Programming Guide: In the "Lazy Evaluation" section

it states

"All transformations in Spark are lazy

in that they do not compute their results right away. Instead

they just remember the transformations applied to some base dataset... The transformations are only computed when an action requires a result to be returned to the driver program."

Source: Apache Spark Documentation

RDD Programming Guide

Section: Lazy Evaluation.

2. Databricks Documentation

Introduction to Apache Spark: "Apache Spark uses lazy evaluation for transformations. Transformations are lazy operations

meaning that they are not executed until an action is called. This allows Spark to optimize the query plan by pipelining transformations."

Source: Databricks Documentation

"Developer tools

languages

and APIs"

"Introduction to Apache Spark".

3. Zaharia

M.

et al. (2010). Spark: Cluster Computing with Working Sets. This foundational academic paper on Spark states: "RDDs support two types of operations: transformations

which create a new dataset from an existing one

and actions

which return a value to the driver program after running a computation on the dataset. [...] All transformations in Spark are lazy

in that they do not compute their results right away."

Source: Zaharia

M.

et al. (2010). Spark: Cluster Computing with Working Sets. USENIX HotCloud'10

Page 2

Section 3.1 RDD Operations.

In Apache Spark, the execution hierarchy begins when an action is called on a DataFrame or RDD, which triggers a job. Spark's scheduler analyzes the Directed Acyclic Graph (DAG) of transformations and breaks the job into one or more stages. Stages are sets of computations that can be executed together without a data shuffle. Each stage is then composed of multiple tasks, which are the smallest units of execution. Each task runs on a single executor core and processes a single partition of data. This hierarchical model is Job -> Stage -> Task.

A. This is incorrect because stages contain tasks, not the other way around. A task is the most granular unit of execution.

B. This is incorrect because jobs contain stages. A stage is a subdivision of a job, not a container for multiple jobs.

C. This is incorrect as it inverts the entire hierarchy. A job is the highest-level unit, and a task is the lowest.

1. Apache Spark Official Documentation - Glossary:

Job: "A parallel computation consisting of multiple tasks that gets spawned in response to a Spark action (e.g. save

collect)..."

Stage: "Each job gets divided into smaller sets of tasks called stages that depend on each other..."

Task: "A unit of work that will be sent to one executor."

Source: Apache Spark 3.4.1 Documentation

"Glossary".

2. Databricks Documentation - Spark UI - Jobs Tab:

"The Jobs tab displays a summary of all jobs in the Spark application... The job detail page shows a visualization of the DAG. In the DAG

vertices represent the RDDs or DataFrames and the edges represent the operations to be applied... The DAG is also organized into stages." This documentation visually and textually confirms that jobs are broken into stages

which in turn consist of tasks.

Source: Databricks Documentation

"Spark UI - Jobs tab".

3. Learning Spark

2nd Edition (by Databricks employees):

Chapter 13

"How Spark Executes a Program

" page 304: "When the driver runs

it converts the user’s program into units of physical execution called tasks. Each task is a combination of a chunk of data and a computation to be performed on that chunk. All of this is orchestrated by the driver

which launches tasks on the cluster. A set of tasks is called a stage

and a set of stages is called a job."

In Apache Spark, the DataFrameWriter API is used to save a DataFrame to an external storage system. To overwrite existing data, the save mode must be set to "overwrite".

Option A uses the standard method chaining: df.write returns a DataFrameWriter, .mode("overwrite") sets the desired save behavior, and .json("path/to/file") specifies the format and path, triggering the write operation.

Option D uses the generic .save() method. Here, .format("json") specifies the data source type, and the mode="overwrite" argument is passed directly into the .save() method, achieving the same result. Both are valid and documented ways to perform an overwrite operation.

B. df.write.overwrite.json("path/to/file")

The DataFrameWriter API does not have a direct .overwrite method or attribute; the save mode is set exclusively using the .mode() method or as a parameter to .save().

C. df.write.json("path/to/file", overwrite=True)

The format-specific .json() method does not accept an overwrite boolean parameter. Save modes must be configured using the .mode() method prior to the write call.

1. Apache Spark 3.5.1 Official Documentation

pyspark.sql.DataFrameWriter:

For Option A: The documentation shows the mode(saveMode) method

which sets the SaveMode

and the json(path

...) method

which saves the content in JSON format. The standard usage is to chain these: df.write.mode('overwrite').json(path).

For Option D: The documentation for the save(path=None

format=None

mode=None

...) method explicitly lists mode as a parameter. This confirms that df.write.format('json').save(path

mode='overwrite') is a valid syntax.

For Options B and C: The API documentation confirms the absence of a .overwrite method and an overwrite parameter within the .json() method

invalidating these options.

2. Databricks Official Documentation

"Read and write JSON files":

The documentation provides examples for writing data. The standard syntax shown for overwriting is df.write.mode("overwrite").json("/tmp/my-json-file")

which directly supports the correctness of Option A. It also explains the different save modes

including overwrite.

The requirement is to process data in micro-batches with a fixed interval of 5 seconds. The trigger(processingTime='5 seconds') configuration is specifically designed for this purpose. It instructs the Structured Streaming engine to initiate a new micro-batch at the specified interval of processing time. This ensures that data is processed periodically every 5 seconds, which directly fulfills the engineer's requirement for a fixed-interval micro-batching system.

A: trigger(continuous='5 seconds') configures continuous processing mode, which aims for millisecond latency, not fixed-interval micro-batches. The interval here is for checkpointing, not for batch triggering.

B: trigger() is not valid syntax as the method requires an argument. The default behavior (if .trigger() is omitted) processes a new micro-batch as soon as the previous one finishes, not at a fixed interval.

D: trigger(processingTime=5000) is syntactically incorrect. The processingTime parameter expects a string representing the time interval (e.g., '5 seconds'), not an integer representing milliseconds.

---

1. Apache Spark Official Documentation: The Structured Streaming Programming Guide explicitly defines the processingTime trigger.

Source: Apache Spark 3.5.1 Documentation

Structured Streaming Programming Guide

Section: Triggers.

Details: The documentation states

"ProcessingTime trigger: The query will be executed in micro-batch mode

where micro-batches will be kicked off at the user-specified intervals. If processingTime is 0

the query will be executed as fast as possible." It provides the syntax trigger(processingTime='5 seconds'). This directly supports option C and refutes D. It also describes the continuous trigger

confirming why A is incorrect for this scenario.

2. Databricks Official Documentation: The Databricks documentation on Structured Streaming triggers reinforces this concept.

Source: Databricks Documentation

"Configure Structured Streaming triggers".

Details: The section on "Processing time trigger" explains: "If you specify a processing time interval

Structured Streaming will trigger a micro-batch at that interval." It shows the example df.writeStream.trigger(processingTime='10 seconds')

which validates the syntax and functionality described in option C.

Apache Spark operates on the principle of lazy evaluation. It builds a Directed Acyclic Graph (DAG) of transformations (read, filter, select, groupBy) but does not execute any computation. The processing is only triggered when an action is called. In the provided code, count() is the first action invoked on the DataFrame reduceddf. This call forces Spark to execute the entire chain of preceding transformations—from reading the CSV file to filtering, selecting, and grouping—to compute the final result, which is the number of rows in the aggregated DataFrame.

A. filter is a narrow transformation. It only adds a step to the logical execution plan and does not trigger any data processing.

C. groupBy is a wide transformation that defines a shuffle operation. However, like all transformations, it is lazy and does not execute until an action is called.

D. show() is also an action that would trigger execution. However, it appears after the count() action in the code. Therefore, the computation begins when count() is called first.

1. Databricks Documentation - Apache Spark programming with Databricks: In the "Transformations" section

it states

"Transformations are lazy. Code in a notebook cell that defines a DataFrame and transformations does not run until you explicitly call an action." It lists count() and show() as common actions. This confirms that processing starts with the first action

count().

Source: Databricks Documentation

Introduction to structured data in PySpark

Section: "Transformations".

2. Apache Spark 3.4.1 Documentation - RDD Programming Guide: The fundamental concept of lazy evaluation is explained here. "All transformations in Spark are lazy

in that they do not compute their results right away... The transformations are only computed when an action requires a result to be returned to the driver program." The guide lists count() as a primary example of an action.

Source: Apache Spark Official Documentation

RDD Programming Guide

Section: "Basics" -> "Lazy Evaluation".

3. Book: Spark: The Definitive Guide (by Bill Chambers and Matei Zaharia): Chapter 2

"A Gentle Introduction to Spark

" explicitly details this behavior. It explains that transformations like filter() and groupBy() are lazy

and the logical plan is only executed when an action like count() is called.

Source: Chambers

B.

& Zaharia

M. (2018). Spark: The Definitive Guide. O'Reilly Media

Inc. Chapter 2

"The Concept of Lazy Evaluation" (pp. 26-27).

Adaptive Query Execution (AQE) is a query re-optimization framework in Spark SQL that uses runtime statistics to improve query plans. One of its key features is the ability to dynamically switch join strategies. For instance, AQE can change a plan from a sort-merge join to a more efficient broadcast hash join if it observes during execution that one side of the join is small enough to be broadcasted. This runtime adjustment, based on actual intermediate data sizes rather than initial estimates, can significantly enhance performance. AQE also dynamically coalesces shuffle partitions and handles data skew.

B. Collecting and storing persistent table statistics in the metastore is a function of the Cost-Based Optimizer (CBO) using commands like ANALYZE TABLE, not a runtime feature of AQE.

C. AQE re-optimizes query plans at stage boundaries (shuffles). A single-stage job has no shuffles, so AQE has no opportunity to re-plan and improve its performance.

D. Optimizing the layout of Delta files (e.g., file compaction) is a data management operation specific to Delta Lake, performed by commands like OPTIMIZE, and is separate from AQE's query plan optimization.

1. Databricks Documentation

"Adaptive query execution": This document explicitly lists "Dynamically switches join strategies" as a primary feature of AQE. It states

"AQE converts a sort-merge join to a broadcast hash join when the runtime statistics of any join side is smaller than the broadcast hash join threshold."

2. Apache Spark 3.5.0 Documentation

"SQL Guide > Performance Tuning > Adaptive Query Execution": The official Spark documentation details the three main components of AQE. Under the section "Dynamically Switching Join Strategies

" it explains how AQE can demote a sort-merge join to a broadcast hash join based on runtime statistics.

3. Apache Spark 3.5.0 Documentation

"SQL Guide > Performance Tuning > Cost-Based Optimizer": This section describes how Spark uses table-level statistics for query optimization

which are collected via the ANALYZE TABLE command and persisted. This confirms that persistent statistics are part of CBO

not AQE.

4. Databricks Documentation

"Optimize data file layout": This source describes the OPTIMIZE command for Delta Lake

clarifying that it is a file layout and compaction utility

which is distinct from the runtime query plan optimizations performed by AQE.

The spark.executor.cores configuration property specifies the number of CPU cores allocated to each executor process. In Spark's architecture, each core within an executor can run one task at a time. Therefore, this setting directly controls the maximum number of tasks that can be executed concurrently on a single executor, defining its parallel processing capacity. For instance, setting spark.executor.cores to 4 allows an executor to run up to four tasks simultaneously.

B. spark.task.maxFailures: This property defines the number of times a task can be retried upon failure before the entire job is aborted; it relates to fault tolerance, not parallelism.

C. spark.executor.memory: This sets the total amount of heap memory available to an executor. While crucial for task execution, it does not determine the number of concurrent tasks.

D. spark.sql.shuffle.partitions: This configuration controls the number of partitions for data shuffling during wide transformations, which influences the number of tasks in a stage, not the concurrency within a single executor.

---

1. Official Apache Spark Documentation: The documentation explicitly states the purpose of spark.executor.cores.

Source: Apache Spark 3.4.1 Documentation

Configuration Page

Application Properties section.

Reference: Under the property spark.executor.cores

the description reads: "The number of cores to use on each executor. In standalone and YARN mode

this controls the number of concurrent tasks an executor can run."

2. Official Databricks Documentation: Databricks documentation reinforces this concept in its cluster configuration guides.

Source: Databricks Documentation

Cluster configuration best practices.

Reference: In discussions about cluster sizing and tuning

it is explained that the number of executor cores determines the number of task slots available per executor

directly impacting parallelism. For example

the "Worker type" section often links the number of vCPUs to the default spark.executor.cores setting.

3. University Courseware: Academic materials on distributed computing with Spark confirm this fundamental architectural principle.

Source: UC Berkeley

CS 186/286

Introduction to Database Systems (Fall 2018)

Lecture 20: Spark.

Reference: Slide 25 ("Anatomy of a Spark Job") illustrates that an Executor contains multiple "Task Slots" (cores)

and each slot runs one task. The number of these slots is configured by spark.executor.cores.

The Pandas API on Spark is designed for interoperability with the standard PySpark API. To convert a pyspark.pandas.DataFrame to a pyspark.sql.DataFrame, the designated method is .tospark(). This conversion is a lightweight metadata operation that allows developers to seamlessly switch between the two APIs, leveraging the strengths of each. For example, one might use the Pandas-like syntax for data cleaning and then convert to a Spark DataFrame to use Spark's machine learning libraries.

B. psdf.topyspark(): This method does not exist in the Pandas API on Spark. The correct method name is tospark().

C. psdf.topandas(): This method converts the pyspark.pandas.DataFrame into a standard, in-memory pandas.DataFrame, not a distributed pyspark.sql.DataFrame.

D. psdf.todataframe(): This method does not exist in the Pandas API on Spark for this purpose.

1. Apache Spark 3.5.1 Documentation

pyspark.pandas.DataFrame.tospark: "Return a Spark DataFrame. indexcol can be used to specify the column name for the index." This page explicitly documents the tospark() method for this conversion.

Source: Apache Spark Documentation - pyspark.pandas.DataFrame.tospark

2. Databricks Documentation

Pandas API on Spark

Interoperability: "You can convert a Pandas API on Spark DataFrame to a Spark DataFrame by using tospark()." This section confirms the usage of the method within the Databricks environment.

Source: Databricks Documentation - Pandas API on Spark

3. Apache Spark 3.5.1 Documentation

pyspark.pandas.DataFrame.topandas: This reference is provided to contrast with the correct answer. It states that topandas() returns a "pandas DataFrame". This confirms that option C performs a different type of conversion.

Source: Apache Spark Documentation - pyspark.pandas.DataFrame.topandas

A Pandas API on Spark DataFrame is a distributed data structure, with its data partitioned across the memory of multiple worker nodes in a cluster. In contrast, a standard Pandas DataFrame is a single-machine, in-memory structure. The conversion process, typically using the .topandas() method, must collect all the distributed data partitions from the worker nodes and load them into the memory of the single driver node. If the total size of the dataset is larger than the driver's available RAM, this will cause an OutOfMemoryError, leading to job failure. This is a critical risk when handling large-scale data.

A. The operation does the opposite; it collects data from worker nodes to the single driver node, rather than distributing it.

B. The constraint is the driver's available memory, not a fixed number of rows. The operation can fail with few rows if they are very wide.

C. The operation is designed for a complete data transfer. The primary risk is a system failure due to memory overflow, not silent data loss or truncation.

1. Databricks Documentation

"Pandas API on Spark": In the section on interoperability with Pandas

the documentation warns about this specific behavior. It states

"These conversions require collecting all the data into the driver’s memory. Therefore

you should be careful and only do this on a small subset of data."

Source: Databricks Documentation > Apache Spark > Development > Pandas API on Spark > Working with pandas.

2. Apache Spark™ 3.5.1 Documentation

pyspark.pandas.DataFrame.topandas: The official API documentation for the .topandas() method includes a warning note. It explicitly states

"This method should only be used if the resulting Pandas DataFrame is expected to be small

as all the data is loaded into the driver’s memory."

Source: Apache Spark Documentation > API Docs > PySpark > pyspark.pandas.DataFrame.topandas.

3. Book: "Learning Spark

2nd Edition" by Jules S. Damji

et al. (O'Reilly Media): Chapter 12

which covers the Pandas API on Spark

explains the architectural differences. It highlights that operations like .topandas() trigger a collection of all distributed data to the driver

which is a common source of memory errors when not used cautiously on large DataFrames.

Reference: Chapter 12

Section: "Pandas API on Spark".

Adaptive Query Execution (AQE) optimizes query plans at runtime. One of its key features is dynamically changing join strategies. The configuration spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold sets a size threshold for post-shuffle partitions. When AQE determines that all partitions of one side of a join are smaller than this threshold, it can convert what was initially planned as a sort-merge join into a more efficient shuffled hash join. This is because building a hash table for the smaller side in each partition becomes more performant than sorting both sides. The scenario described directly meets this condition, triggering the conversion to a shuffled hash join.

A. A Cartesian join is a cross-product used when no join keys are specified and is unrelated to this AQE optimization threshold.

C. A broadcast nested loop join is a fallback strategy, often for non-equi joins, and is not the target optimization for this specific threshold.

D. A sort-merge join is the likely initial plan, but AQE converts it to a shuffled hash join because the size condition is met.

1. Databricks Documentation

Adaptive query execution: In the section "Optimize joins

" the documentation states: "AQE can convert a sort-merge join to a shuffled hash join when one side of the join is small enough. This is controlled by the configuration spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold."

Source: Databricks Documentation > Optimizations > Adaptive query execution > Optimize joins.

2. Apache Spark 3.x Official Documentation

SQL Guide

Performance Tuning: In the section on Adaptive Query Execution

under "Dynamically switching join strategies

" it explains: "AQE can convert a sort-merge join to a shuffled hash join when the runtime statistics of any join side is smaller than the configured threshold spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold."

Source: Apache Spark Documentation > SQL Guide > Performance Tuning > Adaptive Query Execution > Dynamically switching join strategies.

The primary benefit of the Pandas API on Spark is that it combines the familiar, productive, and feature-rich API of the pandas library with the distributed, parallel processing power of Apache Spark. This allows data scientists to scale their existing pandas code to work on large datasets that exceed the memory of a single machine. By translating pandas API calls into Spark's execution plan, it leverages all the cores across the cluster to execute transformations in a distributed manner, significantly improving performance on big data while maintaining a well-known interface.

A. While the API does reduce the learning curve for Python users familiar with pandas, its main technical benefit is distributed execution, not its language exclusivity.

B. Pandas on Spark is built on Spark's core engine, which uses lazy evaluation. Computations are planned but not executed until an action is called, unlike standard pandas which is eagerly executed.

C. This describes the limitation of standard pandas, which runs on a single node. The purpose of Pandas on Spark is precisely to overcome this single-node bottleneck by distributing work across a cluster.

1. Databricks Documentation

"Pandas API on Spark": "The pandas API on Spark allows you to scale your pandas workload to any size by running it distributed on a Spark cluster. If you are already familiar with pandas

you can be immediately productive with the pandas API on Spark...". This supports the combination of scalability (distributed on a cluster) and the use of a familiar

feature-rich API.

2. Apache Spark™ 3.5.1 Documentation

"pyspark.pandas": "This project makes data scientists more productive when interacting with big data

by implementing the pandas DataFrame API on top of Apache Spark." This directly states the goal is to provide the pandas API on top of Spark for big data

which aligns with executing queries across a cluster using familiar features.

3. Karau

H.

Konwinski

A.

Wendell

P.

& Zaharia

M. (2015). Learning Spark: Lightning-Fast Big Data Analysis. O'Reilly Media

Inc. (Conceptual basis): Chapter 1 discusses the limitations of single-machine processing (like standard pandas) and introduces Spark's model of distributed computation across a cluster as the solution for big data. The Pandas API on Spark is a direct application of this principle

providing a high-level API over the distributed engine.

The saveAsTable method is specifically designed to write the contents of a DataFrame to a persistent table within the metastore. The question requires the operation to fail if the table already exists. This behavior is controlled by the SaveMode. The ErrorIfExists mode (which is the default) ensures that if the target table already exists, an AnalysisException is thrown, and the operation is aborted without any modification. This precisely matches the requirements of the scenario.

B. saveAsTable with mode Overwrite: This mode would drop the existing table and create a new one, which violates the requirement to fail and not modify anything.

C. save with mode Ignore: The save method writes data to a file system path, not a metastore table. Additionally, Ignore mode would silently do nothing if the path exists, instead of failing.

D. save with mode ErrorIfExists: The save method is incorrect because it does not create a managed table in the metastore; it only writes data to a file system path.

1. Apache Spark 3.4.1 Documentation

SQL

Data Sources

Load and Save Functions

Save Modes:

Section: "Save Modes"

Content: The documentation explicitly states for ErrorIfExists (default): "When saving a DataFrame to a data source

if data/table already exists

an exception is expected to be thrown." This confirms the failure behavior.

URL: https://spark.apache.org/docs/3.4.1/sql-data-sources-load-save-functions.html#save-modes

2. Apache Spark 3.4.1 Documentation

API for Scala

org.apache.spark.sql.DataFrameWriter:

Method: saveAsTable(tableName: String)

Content: The documentation for the DataFrameWriter class describes the saveAsTable method as the way to save the content of the DataFrame as the specified table. This distinguishes it from the save(path: String) method

which writes to a path.

URL: https://spark.apache.org/docs/3.4.1/api/scala/org/apache/spark/sql/DataFrameWriter.html#saveAsTable(tableName:String):Unit

3. Databricks Documentation

Create a table:

Section: "Create a table from a DataFrame"

Content: This page provides examples of creating tables using df.write.saveAsTable("tablename")

establishing it as the correct method for the task. It also links to save options

including the different modes.

URL: https://docs.databricks.com/en/tables/create.html#create-a-table-from-a-dataframe

The primary benefit of the Pandas API on Spark is that it combines the familiar, user-friendly syntax and rich functionalities of the pandas library with the distributed computing power of Apache Spark. This allows data professionals to scale their existing pandas workloads to large datasets that would not fit on a single machine. Operations written using the Pandas API are translated into Spark's execution plan and distributed across all available cores and nodes in the cluster, leveraging Spark's parallel processing capabilities for enhanced performance on big data.

B: While the API is for Python and reduces the learning curve for pandas users, its core benefit is performance and scalability, not its language exclusivity.

C: This describes the behavior of the standard pandas library, which runs on a single node. The Pandas API on Spark is specifically designed for distributed, multi-node execution.

D: Spark operates on the principle of lazy evaluation, where transformations are planned but not executed until an action is called. Eager execution is characteristic of standard pandas.

1. Apache Spark™ 3.5.1 Documentation

Pandas API on Spark

Overview: "The pandas API on Spark fills this gap by providing pandas-equivalent APIs that work with Apache Spark. pandas API on Spark is useful not only for pandas users but also PySpark users

because the pandas API on Spark supports many tasks that are difficult to do with PySpark". This highlights the goal of providing pandas features on the scalable Spark engine.

2. Databricks Documentation

Pandas API on Spark: "The Koalas project

now the pandas API on Spark

makes data scientists more productive when interacting with big data

by implementing the pandas DataFrame API on top of Apache Spark." This directly states the purpose is to implement the pandas API on top of Spark for big data productivity.

3. Learning Spark

2nd Edition

Chapter 11: The Pandas API on Spark: "The key goal of the pandas API on Spark is to provide a familiar API for data scientists and engineers already comfortable with pandas to leverage the power of the distributed Spark engine for big data." This confirms the combination of a familiar API with Spark's distributed power.

The dropDuplicates() transformation is the correct and most efficient method for this task. When a list of column names is passed as an argument to this function (e.g., df.dropDuplicates(["eventts", "sensorid", "metricvalue"])), Spark identifies and removes rows that have identical values only in those specified columns. The first encountered row among a set of duplicates is retained, while subsequent duplicates are discarded. This behavior precisely matches the requirement to deduplicate based on the specified three-column key.

A. dropDuplicates on all columns would incorrectly use ingestts and sourcefilepath in the deduplication criteria, failing to remove intended duplicates.

B. dropDuplicates with no arguments is functionally identical to using all columns, which is incorrect for the same reason as option A.

C. A groupBy operation is an intermediate step for aggregation. It returns a GroupedData object, not a deduplicated DataFrame, and is an incomplete operation for this goal.

1. Apache Spark 3.5.1 Official Documentation

pyspark.sql.DataFrame.dropDuplicates:

Reference: "Return a new DataFrame with duplicate rows removed

optionally only considering certain columns. For a static batch DataFrame

it keeps the first row for each set of duplicates." The function signature is DataFrame.dropDuplicates(subset=None)

where subset is an "optional list of column names to consider."

Location: Apache Spark API Docs > pyspark.sql > DataFrame API > pyspark.sql.DataFrame.dropDuplicates.

2. Databricks Official Documentation

"Duplicate records":

Reference: "You can use dropDuplicates to remove duplicate rows from a DataFrame

optionally considering only a subset of columns... The following code drops duplicate rows from a DataFrame

considering only the columns 'name' and 'gender'."

Location: Databricks Documentation > Apache Spark > DataFrames > Transformations > Duplicate records.

3. University of California

Berkeley - CS 194

Data Science Courseware:

Reference: Lecture materials on Spark DataFrames often explain that df.dropDuplicates(['col1'

'col2']) is the standard method for removing duplicate records based on a subset of columns

contrasting it with df.distinct() which operates on all columns.

Location: Found in typical course materials for data engineering with Spark

such as UC Berkeley's Data Science curriculum resources.

While the question asks for a "UDF implementation," the provided correct answer, Option B, utilizes the built-in, highly-optimized length function from pyspark.sql.functions. In a certification context, this is considered the correct approach because a core principle of Spark development is to always prefer native functions over User-Defined Functions (UDFs) for performance. Python UDFs introduce significant overhead due to data serialization/deserialization between the JVM and a Python process. Therefore, the most appropriate and performant implementation to calculate string length is the native length function, making Option B the best answer.

A. This option has incorrect syntax. The function to create a UDF for DataFrame operations is imported from pyspark.sql.functions, not called via spark.udf. It also specifies an incorrect return type (StringType).

C. This correctly registers a UDF named stringLength for use in Spark SQL queries (e.g., SELECT stringLength(col) ...), but it does not show the function being applied to a DataFrame column using the DataFrame API.

D. This option demonstrates the correct pattern for applying a UDF to a DataFrame column but specifies the wrong return type. The length of a string is an integer, so the return type should be IntegerType(), not StringType().

1. Apache Spark 3.5.1 Documentation

pyspark.sql.functions.length: This official documentation confirms that length is a built-in function that "computes the character length of string data". This supports Option B as the correct native implementation.

Source: spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.length.html

2. Databricks Documentation

"User-defined functions (UDFs)": This documentation explicitly warns about the performance cost of UDFs: "UDFs are a black box for the optimizer... Because the optimizer does not have visibility into the logic of the UDF

it cannot use any of its strategies to optimize the computation... It is recommended to use the built-in functions... before falling back to UDFs." This justifies choosing the built-in function (Option B) over a UDF implementation.

Source: Databricks Documentation > Apache Spark > Development > User-defined functions (UDFs)

3. Apache Spark 3.5.1 Documentation

pyspark.sql.functions.udf: This document details the creation of a UDF for DataFrame use. It shows that the second argument is the returnType

confirming that StringType() in Option D is incorrect for a function that returns an integer length.

Source: spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.udf.html

4. Apache Spark 3.5.1 Documentation

pyspark.sql.UDFRegistration.register: This page shows the correct syntax for registering a UDF for use in the SQL namespace

as demonstrated in Option C. This confirms that Option C is a valid way to define a UDF for SQL

but not to apply it in the DataFrame API as shown in the other options.

Source: spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.UDFRegistration.register.html

By default, spark.read.text() reads files on a line-by-line basis, creating one DataFrame record for each line. To change this behavior and ingest the entire content of each file into a single record, the wholetext option must be set to true. This instructs the text data source reader to treat each file as a single, complete record. The correct implementation would be spark.read.option("wholetext", "true").text(txtpath). The inputfilename() function can then be used as in the original code to add the source file path to each record.

B. The lineSep option specifies a custom line separator but still results in one record per separated segment, not one record for the entire file.

C. Setting wholetext=False explicitly enforces the default behavior, which is to read one record per line, failing to meet the requirement.

D. This is a specific use of the lineSep option. It would split the file content by ", ", which does not guarantee reading the whole file into one record.

1. Apache Spark 3.5.1 Documentation

SQL Guide

Data Sources

Text Files: In the "Data Source Option" section

the wholetext option is described: "If true

reads each file as a single row. The default value is false." This directly supports the correct answer.

Source: Apache Spark Documentation - Text Files

2. Databricks Documentation

Data sources

Text files: The documentation lists available options for reading text files

including wholetext. It states: "When set to true

reads each file as a single row. This is incompatible with lineSep." This confirms that wholetext is the correct option for the specified task.

Source: Databricks Documentation - Text files

In the Apache Spark architecture, the Driver Program creates a SparkContext, which connects to a cluster manager. The cluster manager allocates resources on worker nodes. On each worker node, an Executor process is launched. The driver sends tasks—the units of work—to these executors. The executors are the components directly responsible for carrying out these tasks, performing the actual data processing, and returning the results to the driver. They are the "workers" of a Spark application.

A. Driver Nodes: The driver node orchestrates the overall job, schedules tasks, and communicates with the cluster manager, but it does not execute the distributed tasks itself.

C. CPU Cores: CPU cores are the physical hardware resources that executors use to run tasks in parallel. They are not a logical component of the Spark architecture in the same way an executor is.

D. Worker Nodes: A worker node is a machine that hosts one or more executor processes. While tasks run on worker nodes, the executor is the specific process responsible for the execution.

---

1. Databricks Documentation

"Apache Spark architecture": "An executor is a process launched for an application on a worker node. The executor runs tasks and returns data to the driver. It also provides in-memory storage for RDDs that are cached by user programs through block manager." This source explicitly states that the executor runs tasks.

2. Apache Spark Documentation

"Cluster Mode Overview": "Each application gets its own executor processes

which stay up for the duration of the whole application and run tasks in multiple threads." This official documentation confirms that executors are the processes that run tasks.

3. Karau

H.

Konwinski

A.

Wendell

P.

& Zaharia

M. (2015). Learning Spark: Lightning-Fast Big Data Analysis. O'Reilly Media

Inc. Chapter 4

"Spark Programming Model"

Page 52: "The actual tasks are executed on a set of worker machines. These tasks are run inside of an executor process on each worker." This book

co-authored by the creator of Spark

clearly defines the role of the executor.

When reading from a directory of Parquet files, Apache Spark by default uses the schema from one of the files (a summary file or the first file found) and applies it to all others. This can lead to the omission of columns present only in other files. To address the requirement of including all columns from all files with differing but compatible schemas, the mergeSchema option must be set to True. This instructs Spark to scan all the files, infer a merged schema that encompasses all columns from every file, and read the data accordingly. Columns not present in a particular file will be filled with null values in the resulting DataFrame.

A. This is the default read operation. It does not merge schemas and will likely drop columns that are not present in the schema of the first file Spark inspects.

C. The inferSchema option is irrelevant for Parquet. Parquet is a self-describing format, meaning the schema is stored within the files themselves, so it does not need to be inferred.

D. The option mergeAllCols is not a valid or recognized configuration option for the Apache Spark Parquet data source reader.

1. Apache Spark Official Documentation: In the "Spark SQL

DataFrames and Datasets Guide

" the section on Parquet Files explicitly describes this functionality. It states

"The Parquet data source is now able to automatically detect this case and merge schemas of all these files." It then provides the example: val mergedDF = spark.read.option("mergeSchema"

"true").parquet("data/testtable").

Source: Apache Spark 3.5.1 Documentation

SQL

DataFrames and Datasets Guide -> Data Sources -> Parquet Files -> Schema Merging.

2. Databricks Documentation: The Databricks documentation on data sources confirms this behavior. Under the Parquet options

mergeSchema is described as the method to use when Parquet files in a directory have different schemas and a merged schema is desired.

Source: Databricks Documentation

Read and write Parquet files -> Parquet options -> mergeSchema.

3. Book: Spark: The Definitive Guide (by Bill Chambers and Matei Zaharia): Chapter 9

"Data Sources

" discusses reading Parquet files. It explains that to handle schema evolution across different files

you must enable the mergeSchema option. "To do this

you just need to set one option: mergeSchema to true."

Source: Chambers

B.

& Zaharia

M. (2018). Spark: The Definitive Guide. O'Reilly Media

Inc. Chapter 9

Page 188.

The broadcast() function is a join hint that instructs Spark's query optimizer to use a broadcast hash join strategy. This strategy is highly efficient when joining a large DataFrame with a much smaller one. The smaller DataFrame (df1) is collected by the driver and then replicated (broadcasted) to every executor node in the cluster. Each executor can then perform the join locally against its partitions of the large DataFrame (df2). This avoids the expensive shuffle operation that would otherwise be required to redistribute the keys of the large DataFrame across the network, significantly improving performance.

A. The broadcast() function is an optimization hint for the join execution plan; it does not perform data filtering.

B. broadcast() affects how data is distributed for the join operation, not the configured partition size of the DataFrames.

D. The join condition (on='id') and join type (how='inner') determine which rows match, not the broadcast() hint.

1. Databricks Documentation

"Broadcast hash joins": "When you join a large table to a small table

a broadcast hash join is usually more performant than other join algorithms. A broadcast hash join broadcasts the small table to all nodes in the cluster and performs a hash join locally on each node. The main benefit of a broadcast hash join is that it avoids shuffling the large table."

2. Apache Spark 3.5.1 Documentation

"Join Optimization": "Broadcast hash join is used when one side of the join is small... Spark can broadcast the smaller join side to all executors. This avoids a shuffle of the data in both join sides." The documentation further explains that the broadcast hint can be used to explicitly force this strategy.

Adaptive Query Execution (AQE) is a performance optimization feature in Apache Spark 3 that re-optimizes query execution plans during runtime. It uses statistics from completed stages of the query to make adjustments. Key benefits include dynamically coalescing shuffle partitions to handle small data partitions, switching join strategies (e.g., from sort-merge join to broadcast hash join) if one side of the join is smaller than anticipated, and optimizing joins to handle data skew. These runtime adaptations lead to significant improvements in overall query performance by addressing issues that are not apparent during the initial static planning phase.

A. This describes the standard Catalyst optimizer, which creates a static plan before execution. AQE's core benefit is its ability to adapt during runtime.

C. While Spark parallelizes tasks, this option incorrectly claims AQE does not adjust for runtime metrics like data skew, which is one of its primary functions.

D. This describes the general task scheduling and distribution of Spark, not the specific runtime query plan re-optimization provided by AQE.

1. Databricks Documentation

"Adaptive query execution": "Adaptive query execution (AQE) is query re-optimization that occurs during query execution... Databricks enables AQE by default. It includes three main features: Dynamically coalescing shuffle partitions

Dynamically switching join strategies

and Dynamically optimizing skew joins." This directly supports the runtime adjustments mentioned in option B.

2. Apache Spark 3.5.0 Documentation

SQL Guide

"Adaptive Query Execution": "Spark SQL can reoptimize the query plan in the middle of the execution based on the runtime statistics... As of Spark 3.2

AQE is enabled by default... AQE provides the following features: Coalescing Post Shuffle Partitions

Converting sort-merge join to broadcast join

[and] Skew Join Optimization." This confirms that AQE operates at runtime to handle skewed data and optimize joins.