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Databricks Certified Associate Developer for Apache Spark 3.5 – Python

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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Sample Questions – Free Practice Test & Real Exam Prep

Question #1

41 of 55. A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on. The DataFrame has columns: id | Name | count | timestamp --------------------------------- 1 | USA | 10 2 | India | 20 3 | England | 50 4 | India | 50 5 | France | 20 6 | India | 10 7 | USA | 30 8 | USA | 40 Which code fragment should the engineer use to sort the data in the Name and count columns?

  • A. df1.orderBy(col("count").desc(), col("Name").asc()) 
  • B. df1.sort("Name", "count") 
  • C. df1.orderBy("Name", "count") 
  • D. df1.orderBy(col("Name").desc(), col("count").asc()) 
Answer: A 
 
Explanation:  
To sort a Spark DataFrame by multiple columns, use .orderBy() (or .sort()) with column expressions. 
Correct syntax for descending and ascending mix: 
from pyspark.sql.functions import col 
df1.orderBy(col("count").desc(), col("Name").asc()) 
This sorts primarily by count in descending order and secondarily by Name in ascending order 
(alphabetically). 
Why the other options are incorrect: 
B/C: Default sort order is ascending; wont place highest counts first. 
D: Reverses sorting logic ” sorts Name descending, not required. 
Reference:
Databricks Exam Guide (June 2025): Section œUsing Spark DataFrame APIs  ” sorting, ordering, and 
column expressions. 
Question #2

41 of 55. A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on. The DataFrame has columns: id | Name | count | timestamp --------------------------------- 1 | USA | 10 2 | India | 20 3 | England | 50 4 | India | 50 5 | France | 20 6 | India | 10 7 | USA | 30 8 | USA | 40 Which code fragment should the engineer use to sort the data in the Name and count columns?

  • A. df1.orderBy(col("count").desc(), col("Name").asc()) 
  • B. df1.sort("Name", "count") 
  • C. df1.orderBy("Name", "count") 
  • D. df1.orderBy(col("Name").desc(), col("count").asc()) 
Answer: A 
Explanation:  
To compare a column value with a Python literal constant in a DataFrame expression, use F.lit() to 
convert it into a Spark literal. 
Correct refactor: 
from pyspark.sql import functions as F 
min_price = 110.50 
result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*")) 
This avoids type mismatches and ensures Spark executes the filter expression on the cluster. 
Why the other options are incorrect: 
B: where() syntax is valid, but F.lit("price") is incorrect ” wraps string literal, not a column. 
C: withColumn adds a column, not needed for this aggregation. 
D: Comparison logic reversed. 
Reference: 

Question #3

40 of 55. A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5. The original code: from pyspark.sql import functions as F min_price = 110.50 result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?

  • A. result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))  
  • B. result_df = prices_df.where(F.lit("price") > min_price).groupBy().count() 
  • C. result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True)) 
  • D. result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count() 
Answer: A 
Explanation:  
To compare a column value with a Python literal constant in a DataFrame expression, use F.lit() to 
convert it into a Spark literal. 
Correct refactor: 
from pyspark.sql import functions as F 
min_price = 110.50 
result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*")) 
This avoids type mismatches and ensures Spark executes the filter expression on the cluster. 
Why the other options are incorrect: 
B: where() syntax is valid, but F.lit("price") is incorrect ” wraps string literal, not a column. 
C: withColumn adds a column, not needed for this aggregation. 
D: Comparison logic reversed. 
Reference:
PySpark SQL Functions ” lit(), col(), and DataFrame filters. 
Databricks Exam Guide (June 2025): Section œDeveloping Apache Spark DataFrame/DataSet API 
Applications  ” filtering, literals, and aggregations. 
Question #4

39 of 55. A Spark developer is developing a Spark application to monitor task performance across a cluster. One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis. Which technique should the developer use? 

  • A. Broadcast a variable to share the maximum time among workers. 
  • B. Configure the Spark UI to automatically collect maximum times. 
  • C. Use an RDD action like reduce() to compute the maximum time. 
  • D. Use an accumulator to record the maximum time on the driver. 
Answer: C 
Explanation:  
RDD actions like reduce() aggregate values across all partitions and return the result to the driver. 
To compute the maximum processing time, reduce() is ideal because it combines results from all 
tasks efficiently. 
Example: 
max_time = rdd_times.reduce(lambda x, y: max(x, y)) 
This aggregates maximum values from all executors into a single result on the driver. 
Why the other options are incorrect: 
A: Broadcast variables distribute read-only data; they cannot aggregate results.
B: Spark UI provides visualization, not programmatic collection. 
D: Accumulators support additive operations only (e.g., counters, sums), not non-associative ones 
like max. 
Reference: 
Spark RDD API ” reduce() for aggregations. 
Databricks Exam Guide (June 2025): Section œApache Spark Architecture and Components  ” 
actions, accumulators, and broadcast variables. 
==========
Question #5

38 of 55. A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json. The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that: Reads directly from /data/input.json. Infers the schema automatically. Merges differing schemas. Which code snippet should the engineer use? A. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true'); B. CREATE TABLE users USING json OPTIONS (path '/data/input.json'); C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', inferSchema 'true'); D. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeAll 'true'); 

  • A. Option A 
  • B. Option B 
  • C. Option C 
  • D. Option D
Answer: A 
Explanation:  
To handle JSON files with evolving or differing schemas, Spark SQL supports the option mergeSchema 
'true', which merges all fields across files into a unified schema.
Correct syntax: 
CREATE EXTERNAL TABLE users 
USING json 
OPTIONS (path '/data/input.json', mergeSchema 'true'); 
This creates an external table directly on the JSON data, inferring schema automatically and merging 
variations. 
Why the other options are incorrect: 
B: Missing schema merge configuration ” fails with inconsistent files. 
C: inferSchema applies to CSV/other file types, not JSON. 
D: mergeAll is not a valid Spark SQL option. 
Reference: 
Spark SQL Data Sources ” JSON file options (mergeSchema, path). 
Databricks Exam Guide (June 2025): Section œUsing Spark SQL  ” creating external tables and 
schema inference for JSON data. 
Question #6

37 of 55. 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 = customerDF \ .withColumn("username", split(col("email"), "@").getItem(0)) \ .withColumn("domain", split(col("email"), "@").getItem(1)) B. customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", "")) C. customerDF = customerDF.select("email").alias("username", "domain") D. customerDF = customerDF.withColumn("domain", col("email").split("@")[1]) 

  • A. Option A 
  • B. Option B 
  • C. Option C 
  • D. Option D
Answer: A 
Explanation:  
The split() function in PySpark splits strings into an array based on a given delimiter. 
Then, .getItem(index) extracts a specific element from the array. 
Correct usage: 
from pyspark.sql.functions import split, col 
customerDF = customerDF \ 
.withColumn("username", split(col("email"), "@").getItem(0)) \ 
.withColumn("domain", split(col("email"), "@").getItem(1)) 
This creates two new columns derived from the email field: 
"username" → text before @ 
"domain" → text after @ 
Why the other options are incorrect: 
B: regexp_replace only replaces text; does not split into multiple columns. 
C: .select() cannot alias multiple derived columns like this. 
D: Column objects are not native Python strings; cannot use standard .split(). 
Reference: 
PySpark SQL Functions ” split() and getItem(). 
Databricks Exam Guide (June 2025): Section œDeveloping Apache Spark DataFrame/DataSet API 
Applications  ” manipulating and splitting column data.
Question #7

36 of 55. What is the main advantage of partitioning the data when persisting tables? 

  • A. It compresses the data to save disk space. 
  • B. It automatically cleans up unused partitions to optimize storage. 
  • C. It ensures that data is loaded into memory all at once for faster query execution. 
  • D. It optimizes by reading only the relevant subset of data from fewer partitions. 
Answer: D 
Explanation:  
Partitioning a dataset divides data into separate directories based on partition column values. When 
queries filter on partitioned columns, Spark can prune irrelevant partitions ” meaning it only reads 
files that match the filter criteria. 
Advantage: 
Reduces I/O and improves performance by scanning only relevant subsets of data. 
Example: 
/data/sales/year=2023/month=10/... 
/data/sales/year=2024/month=01/... 
A query filtering WHERE year = 2024 reads only the relevant partition. 
Why the other options are incorrect: 
A: Compression is independent of partitioning. 
B: Spark does not automatically clean partitions unless managed manually. 
C: Partitioning does not cause Spark to load entire data into memory. 
Reference: 
Databricks Exam Guide (June 2025): Section œUsing Spark SQL  ” partitioning and pruning for 
optimized data retrieval. 
Spark SQL Documentation ” DataFrameWriter partitionBy() and query optimization.
Question #8

35 of 55. A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off. How can this be achieved? 

  • A. By configuring the option recoveryLocation during SparkSession initialization. 
  • B. By configuring the option checkpointLocation during readStream. 
  • C. By configuring the option checkpointLocation during writeStream. 
  • D. By configuring the option recoveryLocation during writeStream. 
Answer: C 
Explanation:  
In Structured Streaming, checkpoints store state information (offsets, progress, and metadata) 
needed to resume a stream after a failure or restart. 
Correct usage: 
Set the checkpointLocation option when writing the streaming output: 
streaming_df.writeStream \ 
.format("delta") \ 
.option("checkpointLocation", "/path/to/checkpoint/dir") \ 
.start("/path/to/output") 
Spark uses this checkpoint directory to recover progress automatically and maintain exactly-once 
semantics. 
Why the other options are incorrect: 
A/D: recoveryLocation is not a valid Spark configuration option. 
B: Checkpointing must be configured in writeStream, not during readStream. 
Reference: 
PySpark Structured Streaming Guide ” Checkpointing and recovery 
Databricks Exam Guide (June 2025): Section œStructured Streaming  ” explains checkpointing and 
fault-tolerant streaming recovery.
Question #9

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. Set the spark.network.timeout property to allow tasks more time to complete without being killed. 
  • B. Increase the executor memory allocation in the Spark configuration. 
  • C. Reduce the size of the data partitions to improve task scheduling. 
  • D. Increase the number of executor instances to handle more concurrent tasks. 
Answer: D 
Explanation:  
Underutilization with timeout warnings often indicates insufficient parallelism ” meaning there 
arent 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 wont fix scheduling bottlenecks. 
C: Reducing partition size may increase overhead and does not fix resource imbalance. 
Reference: 
Databricks Exam Guide (June 2025): Section œTroubleshooting and Tuning Apache Spark DataFrame 
API Applications  ” tuning executors and cluster utilization. 
Spark Configuration ” executor instances and resource scaling.
Question #10

33 of 55. The data engineering team created a pipeline that extracts data from a transaction system. The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the œAmerica/New_York  timezone for reporting. Which code should be used to convert the timestamp to the target timezone? A. raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York")) B. raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York")) C. raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York")) D. raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))

  • A. Option A 
  • B. Option B 
  • C. Option C 
  • D. Option D 
Answer: A 
Explanation:  
In Spark SQL, to convert a UTC timestamp to another timezone, you use the function 
from_utc_timestamp(). 
Correct syntax: 
from pyspark.sql.functions import from_utc_timestamp, col 
df_converted = raw.withColumn( 
"transaction_datetime", 
from_utc_timestamp(col("transaction_datetime"), "America/New_York") 
) 
This adjusts the UTC time into the specified timezone using Sparks timezone database. 
Why the other options are incorrect: 
B: to_utc_timestamp() converts local time to UTC, not the other way around. 
C: date_format() formats timestamps as strings but doesnt adjust timezones. 
D: convert_timezone() is not a valid Spark SQL function. 
Reference: 
Spark SQL Functions ” from_utc_timestamp() and to_utc_timestamp(). 
Databricks Exam Guide (June 2025): Section œUsing Spark SQL  ” working with timestamps and 
timezone conversions. 

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