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NEW QUESTION # 40
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)
B)
C)
D)
Options:
- A. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
- B. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
- C. Uses trigger(continuous='5 seconds') - continuous processing mode.
- D. Uses trigger() - default micro-batch trigger without interval.
Answer: A
Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream \
outputMode("append") \
trigger(processingTime='5 seconds') \
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers
NEW QUESTION # 41
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
The resulting Python dictionary must contain a mapping of region-> region id containing the smallest 3 region_idvalues.
Which code fragment meets the requirements?
A)
B)
C)
D)
The resulting Python dictionary must contain a mapping ofregion -> region_idfor the smallest
3region_idvalues.
Which code fragment meets the requirements?
- A. regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
) - B. regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
) - C. regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
) - D. regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The question requires creating a dictionary where keys areregionvalues and values are the correspondingregion_idintegers. Furthermore, it asks to retrieve only the smallest 3region_idvalues.
Key observations:
select('region', 'region_id')puts the column order as expected bydict()- where the first column becomes the key and the second the value.
sort('region_id')ensures sorting in ascending order so the smallest IDs are first.
take(3)retrieves exactly 3 rows.
Wrapping the result indict(...)correctly builds the required Python dictionary:{ 'AFRICA': 0, 'AMERICA': 1,
'ASIA': 2 }.
Incorrect options:
Option B flips the order toregion_idfirst, resulting in a dictionary with integer keys - not what's asked.
Option C uses.limit(3)without sorting, which leads to non-deterministic rows based on partition layout.
Option D sorts in descending order, giving the largest rather than smallestregion_ids.
Hence, Option A meets all the requirements precisely.
NEW QUESTION # 42
A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?
- A. final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events") - B. final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events") - C. final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events") - D. final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
Answer: C
Explanation:
To ensure that data written out to disk is sorted, it is important to consider how Spark writes data when saving to Parquet tables. The methods .sort() or .orderBy() apply a global sort but do not guarantee that the sorting will persist in the final output files unless certain conditions are met (e.g. a single partition via .coalesce(1) - which is not scalable).
Instead, the proper method in distributed Spark processing to ensure rows are sorted within their respective partitions when written out is:
.sortWithinPartitions("column_name")
According to Apache Spark documentation:
"sortWithinPartitions() ensures each partition is sorted by the specified columns. This is useful for downstream systems that require sorted files." This method works efficiently in distributed settings, avoids the performance bottleneck of global sorting (as in .orderBy() or .sort()), and guarantees each output partition has sorted records - which meets the requirement of consistently sorted data.
Thus:
Option A and B do not guarantee the persisted file contents are sorted.
Option C introduces a bottleneck via .coalesce(1) (single partition).
Option D correctly applies sorting within partitions and is scalable.
NEW QUESTION # 43
The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?
- A. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159 - B. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159 - C. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159 - D. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
Answer: B
Explanation:
The original code has several issues:
It references a variable answer that is undefined.
The function is annotated to return a str, but the logic attempts numeric multiplication.
The UDF return type is declared as T.IntegerType() but the function performs a floating-point operation, which is incompatible.
Option B correctly:
Uses DoubleType to reflect the fact that the multiplication involves a float (3.14159).
Declares the input as float, which aligns with the multiplication.
Returns a float, which matches both the logic and the schema type annotation.
This structure aligns with how PySpark expects User Defined Functions (UDFs) to be declared:
"To define a UDF you must specify a Python function and provide the return type using the relevant Spark SQL type (e.g., DoubleType for float results)." Example from official documentation:
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType
@udf(returnType=DoubleType())
def multiply_by_pi(x: float) -> float:
return x * 3.14159
This makes Option B the syntactically and semantically correct choice.
NEW QUESTION # 44
A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)
- A. Execute their pyspark shell with the option --remote "sc://localhost"
- B. Execute their pyspark shell with the option --remote "https://localhost"
- C. Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
- D. Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
- E. Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
Answer: A,E
Explanation:
Spark Connect enables decoupling of the client and Spark driver processes, allowing remote access. Spark supports configuring the remote Spark Connect server in multiple ways:
From Databricks and Spark documentation:
Option B (--remote "sc://localhost") is a valid command-line argument for the pyspark shell to connect using Spark Connect.
Option C (setting SPARK_REMOTE environment variable) is also a supported method to configure the remote endpoint.
Option A is incorrect because Spark Connect uses the sc:// protocol, not https://.
Option D requires modifying the code, which the question explicitly avoids.
Option E configures the port on the server side but doesn't start a client connection.
Final Answers: B and C
NEW QUESTION # 45
A developer runs:
What is the result?
Options:
- A. It throws an error if there are null values in either partition column.
- B. It creates separate directories for each unique combination of color and fruit.
- C. It stores all data in a single Parquet file.
- D. It appends new partitions to an existing Parquet file.
Answer: B
Explanation:
The partitionBy() method in Spark organizes output into subdirectories based on unique combinations of the specified columns:
e.g.
/path/to/output/color=red/fruit=apple/part-0000.parquet
/path/to/output/color=green/fruit=banana/part-0001.parquet
This improves query performance via partition pruning.
It does not consolidate into a single file.
Null values are allowed in partitions.
It does not "append" unless .mode("append") is used.
NEW QUESTION # 46
49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?
- A. REPLACE
- B. APPEND
- C. COMPLETE
- D. AGGREGATE
Answer: C
Explanation:
Structured Streaming supports three output modes:
Append: Writes only new rows since the last trigger.
Update: Writes only updated rows.
Complete: Writes the entire result table after every trigger execution.
For aggregations like groupBy().count(), only complete mode outputs the entire table each time.
Example:
aggDF.writeStream \
.outputMode("complete") \
.format("console") \
.start()
Why the other options are incorrect:
A: "AGGREGATE" is not a valid output mode.
C: "REPLACE" does not exist.
D: "APPEND" writes only new rows, not the full table.
Reference:
PySpark Structured Streaming - Output Modes (append, update, complete).
Databricks Exam Guide (June 2025): Section "Structured Streaming" - output modes and use cases for aggregations.
NEW QUESTION # 47
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. Enable dynamic resource allocation to scale resources as needed
- B. Increase the size of the dataset to create more partitions
- C. Reduce the value of spark.sql.shuffle.partitions
- D. Increase the value of spark.sql.shuffle.partitions
Answer: D
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 # 48
34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?
- A. Increase the executor memory allocation in the Spark configuration.
- B. Set the spark.network.timeout property to allow tasks more time to complete without being killed.
- C. Increase the number of executor instances to handle more concurrent tasks.
- D. Reduce the size of the data partitions to improve task scheduling.
Answer: C
Explanation:
Underutilization with timeout warnings often indicates insufficient parallelism - meaning there aren't enough executors to process all tasks concurrently.
Solution:
Increase the number of executors to allow more parallel task execution and better resource utilization.
Example configuration:
--conf spark.executor.instances=8
This distributes the workload more effectively across cluster nodes and reduces idle time for pending tasks.
Why the other options are incorrect:
A: Extending timeouts hides the symptom, not the root cause (lack of executors).
B: More memory per executor won't fix scheduling bottlenecks.
C: Reducing partition size may increase overhead and does not fix resource imbalance.
Reference:
Databricks Exam Guide (June 2025): Section "Troubleshooting and Tuning Apache Spark DataFrame API Applications" - tuning executors and cluster utilization.
Spark Configuration - executor instances and resource scaling.
NEW QUESTION # 49
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:
- A. Use the distinct() transformation to combine similar partitions
- B. Use the sortBy() transformation to reorganize the data
- C. Use the repartition() transformation with a lower number of partitions
- D. Use the coalesce() transformation with a lower number of partitions
Answer: D
Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce
NEW QUESTION # 50
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?
- A. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
- B. spark.conf.set("spark.pandas.arrow.enabled", "true")
- C. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
- D. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
Answer: D
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 # 51
10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?
- A. It runs on a single node only, utilizing memory efficiently.
- B. It is available only with Python, thereby reducing the learning curve.
- C. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
- D. It computes results immediately using eager execution.
Answer: C
Explanation:
Pandas API on Spark provides a distributed implementation of the Pandas DataFrame API on top of Apache Spark.
Advantages:
Executes transformations in parallel across all nodes and cores in the cluster.
Maintains Pandas-like syntax, making it easy for Python users to transition.
Enables scaling of existing Pandas code to handle large datasets without memory limits.
Therefore, it combines Pandas usability with Spark's distributed power, offering both speed and scalability.
Why the other options are incorrect:
B: While it uses Python, that's not its main advantage.
C: It runs distributed across the cluster, not on a single node.
D: Pandas API on Spark uses lazy evaluation, not eager computation.
Reference:
PySpark Pandas API Overview - advantages of distributed execution.
Databricks Exam Guide (June 2025): Section "Using Pandas API on Apache Spark" - explains the benefits of Pandas API integration for scalable transformations.
NEW QUESTION # 52
Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?
- A. Same number as the cluster executors
- B. 0
- C. 1
- D. 2
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The.coalesce(numPartitions)function is used to reduce the number of partitions in a DataFrame. It does not increase the number of partitions. If the specified number of partitions is greater than the current number, it will not have any effect.
From the official Spark documentation:
"coalesce() results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim one or more of the current partitions." However, if you try to increase partitions using coalesce (e.g., from 10 to 20), the number of partitions remains unchanged.
Hence,df.coalesce(20)will still return a DataFrame with 10 partitions.
Reference: Apache Spark 3.5 Programming Guide # RDD and DataFrame Operations # coalesce()
NEW QUESTION # 53
24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?
- A. spark.sql("SELECT schema FROM events.parquet").show()
- B. spark.read.format("parquet").load("events.parquet").show()
- C. spark.read.parquet("events.parquet").printSchema()
- D. spark.sql("SELECT * FROM events.parquet").show()
Answer: C
Explanation:
To view the schema of a Parquet file, you must use the DataFrameReader to load the Parquet data and call the .printSchema() method.
Correct syntax:
spark.read.parquet("events.parquet").printSchema()
This command loads the file metadata (without triggering a full read) and prints the column names, data types, and nullability information in a tree format.
Why the other options are incorrect:
A/D: SQL queries can't directly introspect file schemas.
B: .show() displays data rows, not schema.
Reference:
PySpark DataFrameReader API - read.parquet() and DataFrame.printSchema().
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - describes reading files and examining schemas in Spark SQL and DataFrame APIs.
NEW QUESTION # 54
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?
- A. Cache large DataFrames to persist them in memory.
- B. Optimize the data processing logic by repartitioning the DataFrame.
- C. Modify the Spark configuration to disable garbage collection
- D. Increase the memory allocated to the Spark Driver.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot ofGC overhead limit exceedederrors in the driver logs, it's a sign that the driver is running out of memory."
-Spark Tuning Guide
Why others are incorrect:
Amay help but does not directly address the driver memory shortage.
Bis not a valid action; GC cannot be disabled.
Dincreases memory usage, worsening the problem.
NEW QUESTION # 55
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:
Which block of Spark code can be used to achieve this requirement?
Options:
- A. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
- B. filtered_df = users_raw_df.na.drop(how='all')
- C. filtered_df = users_raw_df.na.drop(how='any')
- D. filtered_df = users_raw_df.na.drop(thresh=0)
Answer: C
Explanation:
na.drop(how='any')drops any row that has at least one null value.
This is exactly what's needed when the goal is to retain only fully complete records.
Usage:CopyEdit
filtered_df = users_raw_df.na.drop(how='any')
Explanation of incorrect options:
A: thresh=0 is invalid - thresh must be # 1.
B: how='all' drops only rows where all columns are null (too lenient).
D: spark.na.drop doesn't support mixing how and thresh in that way; it's incorrect syntax.
Reference:PySpark DataFrameNaFunctions.drop()
NEW QUESTION # 56
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.withColumn()
- D. DataFrame.select()
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 # 57
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