Associate-Developer-Apache-Spark-3.5 Questions Prepare with Learning Information! 2026 Regularly updated
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NEW QUESTION # 70
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?
- A. Increase the value of spark.sql.shuffle.partitions
- B. Enable dynamic resource allocation to scale resources as needed
- C. Increase the size of the dataset to create more partitions
- D. Reduce the value of spark.sql.shuffle.partitions
Answer: A
Explanation:
The number of tasks is controlled by the number of partitions. By default, spark.sql.shuffle.partitions is 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.
From the Spark tuning guide:
"To improve performance, especially for large clusters, increase spark.sql.shuffle.partitions to create more tasks and parallelism." Thus:
A is correct: increasing shuffle partitions increases parallelism
B is wrong: it further reduces parallelism
C is invalid: increasing dataset size doesn't guarantee more partitions D is irrelevant to task count per stage Final answer: A
NEW QUESTION # 71
A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?
- A. employees_df.filter(employees_df.tenure >= 5).show()
- B. employees_df.where(employees_df.tenure >= 5)
- C. filter(employees_df.tenure >= 5)
- D. employees_df.filter(employees_df.tenure >= 5).collect()
Answer: A
Explanation:
To filter rows based on a condition and display them in Spark, use filter(...).show():
employees_df.filter(employees_df.tenure >= 5).show()
Option A is correct and shows the results.
Option B filters but doesn't display them.
Option C uses Python's built-in filter, not Spark.
Option D collects the results to the driver, which is unnecessary if .show() is sufficient.
Final answer: A
NEW QUESTION # 72
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.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.option("inferSchema", "true").parquet("/path/events/data/")
Answer: C
Explanation:
To read all files recursively within a nested directory structure, Spark requires the recursiveFileLookup option to be explicitly enabled. According to Databricks official documentation, when dealing with deeply nested Parquet files in a directory tree (as shown in this example), you should set:
df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/") This ensures that Spark searches through all subdirectories under /path/events/data/ and reads any Parquet files it finds, regardless of the folder depth.
Option A is incorrect because while it includes an option, inferSchema is irrelevant here and does not enable recursive file reading.
Option C is incorrect because wildcards may not reliably match deep nested structures beyond one directory level.
Option D is incorrect because it will only read files directly within /path/events/data/ and not subdirectories like /2023/01/01.
Databricks documentation reference:
"To read files recursively from nested folders, set the recursiveFileLookup option to true. This is useful when data is organized in hierarchical folder structures" - Databricks documentation on Parquet files ingestion and options.
NEW QUESTION # 73
A developer is trying to join two tables,sales.purchases_fctandsales.customer_dim, using the following code:
fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in thepurchases_fcttable that do not exist in thecustomer_dimtable are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?
- A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
- B. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
- C. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
- D. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.
NEW QUESTION # 74
A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?
- A. groupBy
- B. select
- C. coalesce
- D. filter
Answer: A
Explanation:
The groupBy() operation causes a shuffle because it requires all values for a specific key to be brought together, which may involve moving data across partitions.
In contrast:
filter() and select() are narrow transformations and do not cause shuffles.
coalesce() tries to reduce the number of partitions and avoids shuffling by moving data to fewer partitions without a full shuffle (unlike repartition()).
NEW QUESTION # 75
A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?
- A. Set the number of partitions equal to the total number of CPUs in the cluster
- B. Set the number of partitions equal to the number of nodes in the cluster
- C. Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
- D. Set the number of partitions to a fixed value, such as 200
Answer: C
Explanation:
Spark's best practice is to estimate partition count based on data volume and a reasonable partition size - typically 128 MB to 256 MB per partition.
With 1 TB of data: 1 TB / 128 MB ≈ ~8000 partitions
This ensures that tasks are distributed across available CPUs for parallelism and that each task processes an optimal volume of data.
Option A (equal to cores) may result in partitions that are too large.
Option B (fixed 200) is arbitrary and may underutilize the cluster.
Option C (nodes) gives too few partitions (10), limiting parallelism.
NEW QUESTION # 76
42 of 55.
A developer needs to write the output of a complex chain of Spark transformations to a Parquet table called events.liveLatest.
Consumers of this table query it frequently with filters on both year and month of the event_ts column (a timestamp).
The current code:
from pyspark.sql import functions as F
final = df.withColumn("event_year", F.year("event_ts")) \
.withColumn("event_month", F.month("event_ts")) \
.bucketBy(42, ["event_year", "event_month"]) \
.saveAsTable("events.liveLatest")
However, consumers report poor query performance.
Which change will enable efficient querying by year and month?
- A. Add .sortBy() after .bucketBy()
- B. Replace .bucketBy() with .partitionBy("event_year", "event_month")
- C. Change the bucket count (42) to a lower number
- D. Replace .bucketBy() with .partitionBy("event_year") only
Answer: B
Explanation:
When queries frequently filter on certain columns, partitioning by those columns ensures partition pruning, allowing Spark to scan only relevant directories instead of the entire dataset.
Correct code:
final.write.partitionBy("event_year", "event_month").parquet("events.liveLatest") This improves read performance dramatically for filters like:
SELECT * FROM events.liveLatest WHERE event_year = 2024 AND event_month = 5; bucketBy() helps in clustering and joins, not in partition pruning for file-based tables.
Why the other options are incorrect:
B: Bucket count changes parallelism, not query pruning.
C: sortBy organizes data within files, not across partitions.
D: Partitioning by only one column limits pruning benefits.
Reference:
Spark SQL DataFrameWriter - partitionBy() for partitioned tables.
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - partitioning vs. bucketing and query optimization.
NEW QUESTION # 77
21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?
- A. The number of days specified will be removed from the start date.
- B. An error message of an invalid parameter will be returned.
- C. The same start date will be returned.
- D. The number of days specified will be added to the start date.
Answer: D
Explanation:
In Spark SQL, the function date_sub(startDate, days) returns the date that is days before startDate.
If the days parameter is negative, Spark interprets it as subtracting a negative number, which effectively adds days to the date.
Example:
from pyspark.sql.functions import date_sub, lit
df = spark.createDataFrame([( "2024-10-01", )], ["dt"])
df.select(date_sub("dt", -5).alias("new_date")).show()
Output:
+----------+
| new_date |
+----------+
|2024-10-06|
+----------+
Why the other options are incorrect:
B: No error occurs; negative values are supported.
C: The start date changes if days ≠ 0.
D: Subtracting days would move the date backward, not forward.
Reference:
Spark SQL Functions - date_sub(startDate, days) and date_add(startDate, days) behavior.
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - working with date and timestamp functions.
NEW QUESTION # 78
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?
- A. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
- B. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
- C. spark.conf.set("spark.pandas.arrow.enabled", "true")
- D. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
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 # 79
How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:
- A. Set the spark.executor.memory property to a large value.
- B. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
- C. Increase the number of local threads based on the number of CPU cores.
- D. Configure the application to run in cluster mode instead of local mode.
Answer: C
Explanation:
When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.
Using local[*] ensures Spark uses all available CPU cores for parallelism.
Example:
spark-submit --masterlocal[*]
Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.
Reference:Spark Master URLs
NEW QUESTION # 80
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?
- A. It provides a way to run Spark applications remotely in any programming language
- B. It can be used to interact with any remote cluster using the REST API
- C. It allows for remote execution of Spark jobs
- D. It is primarily used for data ingestion into Spark from external sources
Answer: C
Explanation:
Spark Connect introduces a decoupled client-server architecture. Its key feature is enabling Spark job submission and execution from remote clients - in Python, Java, etc.
From Databricks documentation:
"Spark Connect allows remote clients to connect to a Spark cluster and execute Spark jobs without being co-located with the Spark driver." A is close, but "any language" is overstated (currently supports Python, Java, etc., not literally all).
B refers to REST, which is not Spark Connect's mechanism.
D is incorrect; Spark Connect isn't focused on ingestion.
Final answer: C
NEW QUESTION # 81
A developer initializes a SparkSession:
spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?
- A. The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
- B. If a SparkSession already exists, this code will return the existing session instead of creating a new one.
- C. A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
- D. A new SparkSession is created every time the getOrCreate() method is invoked.
Answer: B
Explanation:
According to the PySpark API documentation:
"getOrCreate(): Gets an existing SparkSession or, if there is no existing one, creates a new one based on the options set in this builder." This means Spark maintains a global singleton session within a JVM process. Repeated calls to getOrCreate() return the same session, unless explicitly stopped.
Option A is incorrect: the method does not destroy any session.
Option B incorrectly ties uniqueness to appName, which does not influence session reusability.
Option D is incorrect: it contradicts the fundamental behavior of getOrCreate().
(Source: PySpark SparkSession API Docs)
NEW QUESTION # 82
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. Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
- C. Fetch the logs by running a Spark job with the spark-sql CLI tool.
- D. Locate the executor logs on the Spark master node, typically under the /tmp directory.
Answer: B
Explanation:
The Spark UI is the standard and most effective way to inspect executor logs, task time, input size, and shuffles.
From the Databricks documentation:
"You can monitor job execution via the Spark Web UI. It includes detailed logs and metrics, including task-level execution time, shuffle reads/writes, and executor memory usage." (Source: Databricks Spark Monitoring Guide) Option A is incorrect: logs are not guaranteed to be in /tmp, especially in cloud environments.
B . -verbose helps during job submission but doesn't give detailed executor logs.
D . spark-sql is a CLI tool for running queries, not for inspecting logs.
Hence, the correct method is using the Spark UI → Stages tab → Executor logs.
NEW QUESTION # 83
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. Loss of access to past job logs and reduced debugging capability for completed jobs
- C. Improved job execution speed due to reduced logging overhead
- D. Prevention of driver log accumulation during long-running jobs
Answer: B
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 # 84
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand 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.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
- B. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
- C. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
- D. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Shuffling occurs in operations likegroupBy,reduceByKey, orjoin-which cause data to be moved across partitions. Therepartition()operation can also cause a shuffle, but in this context, it follows an aggregation.
InOption D, thegroupByfollowed byaggresults in a shuffle due to grouping across nodes.
Therepartition(10)is a partitioning transformation but does not involve a new shuffle since the data is already grouped.
This sequence - shuffle (groupBy) followed by non-shuffling (repartition) - is correct.
Option A does the opposite: thefilterdoes not cause a shuffle, butgroupBydoes - this makes it the wrong order.
NEW QUESTION # 85
20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?
- A. Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
- B. persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
- C. cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
- D. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
Answer: C
Explanation:
Both cache() and persist() are Spark DataFrame storage operations that store computed results in memory (and optionally on disk) to speed up subsequent actions on the same DataFrame.
Key difference:
cache() is a shorthand for persist(StorageLevel.MEMORY_AND_DISK).
persist() allows specifying different storage levels, such as MEMORY_ONLY, DISK_ONLY, or MEMORY_AND_DISK_SER.
Example:
df.cache() # Uses MEMORY_AND_DISK by default
df.persist(StorageLevel.MEMORY_ONLY) # Custom storage level
Both trigger caching upon an action (e.g., count(), collect()).
Why the other options are incorrect:
A: persist() default is not DISK_ONLY; default storage level is MEMORY_AND_DISK.
B/C: cache() cannot set arbitrary levels; only persist() can.
Reference:
PySpark API Reference - DataFrame.cache() and DataFrame.persist().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - caching, persistence, and storage levels.
NEW QUESTION # 86
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 is responsible for executing all tasks locally without distributing them across the worker nodes.
- B. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
- C. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
- 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.
Answer: D
Explanation:
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation - Cluster Mode Overview)
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation - Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.
NEW QUESTION # 87
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:
- A. Save the users_vw definition and query using Spark
- B. Recreate the users_vw and query the data using Spark
- C. Query the users_vw using Spark
- D. Persist the users_vw data as a table
Answer: D
Explanation:
Temp views likecreateOrReplaceTempVieware session-scoped.
They disappear once the Spark session ends.
To retain data across sessions, it must be persisted:
df.write.saveAsTable("users_vw")
Thus, the view needs to be persisted as a table to survive session termination.
Reference:Databricks - Temp vs Global vs Permanent Views
NEW QUESTION # 88
A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:
- A. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
- B. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
- C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
- D. CREATE TABLE users USING json OPTIONS (path '/data/input.json')
Answer: C
Explanation:
To create an external table and enable schema merging, the correct syntax is:
CREATE EXTERNAL TABLE users
USING json
OPTIONS (
path '/data/input.json',
mergeSchema 'true'
)
mergeSchema is the correct option key (not schemaMerge)
EXTERNAL allows Spark to query files without managing their lifecycle
NEW QUESTION # 89
6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?
- A. Driver Nodes
- B. Worker Nodes
- C. CPU Cores
- D. Executors
Answer: D
Explanation:
In Spark's distributed architecture:
The Driver Node coordinates the execution of a Spark application. It converts the logical plan into a physical plan of stages and tasks.
The Executors, running on Worker Nodes, are responsible for executing tasks assigned by the driver and storing data (in memory or disk) during execution.
Key point:
Executors are the active agents that perform the actual computations on data partitions. Each executor runs multiple tasks in parallel using available CPU cores.
Why the other options are incorrect:
A (Driver Nodes): The driver schedules tasks; it doesn't execute them.
C (CPU Cores): CPU cores execute within executors, but they are hardware, not Spark architectural components.
D (Worker Nodes): Worker nodes host executors but do not directly execute tasks; executors do.
Reference (Databricks Apache Spark 3.5 - Python / Study Guide):
Spark Architecture Components - Driver, Executors, Cluster Manager, Worker Nodes.
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - describes the roles of driver and executor nodes in distributed processing.
NEW QUESTION # 90
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?
- A. DataFrame.filter()
- B. DataFrame.groupBy().agg()
- C. DataFrame.select()
- D. DataFrame.withColumn()
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) # causes shuffle.
Options B, C, and D (filter, withColumn, select) # transformations that do not require shuffling; they are narrow dependencies.
Final Answer: A
NEW QUESTION # 91
A data engineer needs to write a DataFramedfto a Parquet file, partitioned by the columncountry, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?
- A. df.write.partitionBy("country").parquet("/data/output")
- B. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- C. df.write.mode("overwrite").parquet("/data/output")
- D. df.write.mode("append").partitionBy("country").parquet("/data/output")
Answer: B
Explanation:
The.mode("overwrite")ensures that existing files at the path will be replaced.
partitionBy("country")optimizes queries by writing data into partitioned folders.
Correct syntax:
df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- Source:Spark SQL, DataFrames and Datasets Guide
NEW QUESTION # 92
What is a feature of Spark Connect?
- A. It supports only PySpark applications
- B. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
- C. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
- D. It has built-in authentication
Answer: B
Explanation:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B . While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C . Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D . Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.
NEW QUESTION # 93
......
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