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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.
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
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
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
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.
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.
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:
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.
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
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