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NEW QUESTION # 10
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?
- A. Use an RDD action like reduce() to compute the maximum time
- B. Configure the Spark UI to automatically collect maximum times
- C. Use an accumulator to record the maximum time on the driver
- D. Broadcast a variable to share the maximum time among workers
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such asreduce()oraggregate().
From the documentation:
"To perform global aggregations on distributed data, actions likereduce()are commonly used to collect summaries such as min/max/avg." Accumulators (Option B) do not support max operations directly and are not intended for such analytics.
Broadcast (Option C) is used to send data to workers, not collect from them.
Spark UI (Option D) is a monitoring tool - not an analytics collection interface.
Final Answer: A
NEW QUESTION # 11
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. Recreate the users_vw and query the data using Spark
- B. Persist the users_vw data as a table
- C. Save the users_vw definition and query using Spark
- D. Query the users_vw using Spark
Answer: B
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 # 12
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)
B)
C)
D)
- A. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record")) - B. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
- C. exploded_df = exploded_df.select("record_datetime", "record_exploded")
- D. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C
NEW QUESTION # 13
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('cust_id') == F.col('customer_id'))
- B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
- C. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
- D. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
Answer: B
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 # 14
What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?
- A. persist()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) andcache()- Can be used to set different storage levels to persist the contents of the DataFrame.
- B. cache()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK) andpersist()- Can be used to set different storage levels to persist the contents of the DataFrame
- C. Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.
- D. Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
df.cache()is shorthand fordf.persist(StorageLevel.MEMORY_AND_DISK)
df.persist()allows specifying any storage level such asMEMORY_ONLY,DISK_ONLY, MEMORY_AND_DISK_SER, etc.
By default,persist()usesMEMORY_AND_DISK, unless specified otherwise.
Reference:Spark Programming Guide - Caching and Persistence
NEW QUESTION # 15
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?
- A. customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
) - B. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
) - C. customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1)) - D. customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Option B is the correct and idiomatic approach in PySpark to split a string column (like email) based on a delimiter such as "@".
The split(col("email"), "@") function returns an array with two elements: username and domain.
getItem(0) retrieves the first part (username).
getItem(1) retrieves the second part (domain).
withColumn() is used to create new columns from the extracted values.
Example from official Databricks Spark documentation on splitting columns:
from pyspark.sql.functions import split, col
df.withColumn("username", split(col("email"), "@").getItem(0)) \
withColumn("domain", split(col("email"), "@").getItem(1))
##Why other options are incorrect:
A uses fixed substring indices (substr(0, 5)), which won't correctly extract usernames and domains of varying lengths.
C uses substring_index, which is available but less idiomatic for splitting emails and is slightly less readable.
D removes "@" from the email entirely, losing the separation between username and domain, and ends up duplicating values in both fields.
Therefore, Option B is the most accurate and reliable solution according to Apache Spark 3.5 best practices.
NEW QUESTION # 16
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 runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
- B. 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.
- C. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
- D. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
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) 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 # 17
A developer initializes a SparkSession:
spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes thesparkSparkSession?
- A. ThegetOrCreate()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 eachappName, and callinggetOrCreate()with the same name will return an existing SparkSession once it has been created.
- D. A new SparkSession is created every time thegetOrCreate()method is invoked.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
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 togetOrCreate() return the same session, unless explicitly stopped.
Option A is incorrect: the method does not destroy any session.
Option B incorrectly ties uniqueness toappName, which does not influence session reusability.
Option D is incorrect: it contradicts the fundamental behavior ofgetOrCreate().
(Source:PySpark SparkSession API Docs)
NEW QUESTION # 18
Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?
- A. WARN, NONE, ERROR, FATAL
- B. FATAL, NONE, INFO, DEBUG
- C. ERROR, WARN, TRACE, OFF
- D. ALL, DEBUG, FAIL, INFO
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThesetLogLevel()method ofSparkContextsets the logging level on the driver, which controls the verbosity of logs emitted during job execution. Supported levels are inherited from log4j and include the following:
ALL
DEBUG
ERROR
FATAL
INFO
OFF
TRACE
WARN
According to official Spark and Databricks documentation:
"Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, and WARN." Among the choices provided, only option B (ERROR, WARN, TRACE, OFF) includes four valid log levels and excludes invalid ones like "FAIL" or "NONE".
Reference: Apache Spark API docs # SparkContext.setLogLevel
NEW QUESTION # 19
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?
- A. Data will be lost during conversion
- B. The conversion will automatically distribute the data across worker nodes
- C. The operation will fail if the Pandas DataFrame exceeds 1000 rows
- D. The operation will load all data into the driver's memory, potentially causing memory overflow
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D
NEW QUESTION # 20
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?
A)
B)
C)
D)
- A. result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0)) - B. result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price")) - C. result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price")) - D. result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isBbecause it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.
* F.count_if(condition) counts the number of rows that meet the specified boolean condition.
* In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-
43773)."
Why other options are incorrect or outdated:
* Auses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
* Cperforms a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
* Dincorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore,Bis the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.
NEW QUESTION # 21
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?
- A. A broadcast nested loop join
- B. A shuffled hash join
- C. A sort-merge join
- D. A Cartesian join
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B
NEW QUESTION # 22
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?
- A. It allows for remote execution of Spark jobs
- B. It can be used to interact with any remote cluster using the REST API
- C. It provides a way to run Spark applications remotely in any programming language
- D. It is primarily used for data ingestion into Spark from external sources
Answer: A
Explanation:
Comprehensive and Detailed 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 # 23
A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing agroupByoperation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)
C)
D)
- A. Use theapplyInPandasAPI:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show() - B. Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show() - C. Use themapInPandasAPI:
df.mapInPandas(mean_func, schema="user_id long, value double").show() - D. Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct approach to perform a parallelizedgroupByoperation across Spark worker nodes using Pandas API is viaapplyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.
As per the Databricks documentation:
"applyInPandas()allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution." Option A is correct and achieves this parallel execution.
Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.
Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.
Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.
Therefore, to run agroupBywith parallel Pandas logic on Spark workers, Option A usingapplyInPandasis the only correct answer.
Reference: Apache Spark 3.5 Documentation # Pandas API on Spark # Grouped Map Pandas UDFs (applyInPandas)
NEW QUESTION # 24
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?
- A. saveAsTable with mode Overwrite
- B. save with mode ErrorIfExists
- C. saveAsTable with mode ErrorIfExists
- D. save with mode Ignore
Answer: C
Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A
NEW QUESTION # 25
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='any')
- B. filtered_df = users_raw_df.na.drop(thresh=0)
- C. filtered_df = users_raw_df.na.drop(how='all')
- D. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
Answer: A
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 # 26
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).collect()
- B. filter(employees_df.tenure >= 5)
- C. employees_df.where(employees_df.tenure >= 5)
- D. employees_df.filter(employees_df.tenure >= 5).show()
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To filter rows based on a condition and display them in Spark, usefilter(...).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-infilter, not Spark.
Option D collects the results to the driver, which is unnecessary if.show()is sufficient.
Final Answer: A
NEW QUESTION # 27
Which command overwrites an existing JSON file when writing a DataFrame?
- A. df.write.overwrite.json("path/to/file")
- B. df.write.mode("overwrite").json("path/to/file")
- C. df.write.json("path/to/file", overwrite=True)
- D. df.write.format("json").save("path/to/file", mode="overwrite")
Answer: B
Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
Reference:PySpark DataFrameWriter API
NEW QUESTION # 28
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