Databricks Certified Associate Developer for Apache Spark 3.5 - Python - Associate-Developer-Apache-Spark-3.5 Exam Questions [2026]

QUESTION NO: 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())

Correct Answer: A

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 2
15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A. streaming_df.filter("count < 30") B. streaming_df.show() C. streaming_df.select(countDistinct("name")) D. streaming_df.count()

Correct Answer: A

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 3
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. Replace .bucketBy() with .partitionBy("event_year") only B. Change the bucket count (42) to a lower number C. Replace .bucketBy() with .partitionBy("event_year", "event_month") D. Add .sortBy() after .bucketBy()

Correct Answer: C

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 4
A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate") B. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate") C. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate") D. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

Correct Answer: D

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 5
What is a feature of Spark Connect?

A. It has built-in authentication B. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs C. Supports DataFrame, Functions, Column, SparkContext PySpark APIs D. It supports only PySpark applications

Correct Answer: B

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 6
A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A. 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() B. Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show() C. Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show() D. Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()

Correct Answer: C

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 7
23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A. It processes data solely on disk storage, reducing the need for memory resources. B. It requires specialized hardware to run, making it unsuitable for commodity hardware clusters. C. It eliminates the need to write any code, automatically handling all data processing. D. It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability. E. It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.

Correct Answer: D,E

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 8
Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A. dropDuplicates on the exact matching fields B. groupBy without aggregation (invalid use) C. dropDuplicates on all columns (wrong criteria) D. dropDuplicates with no arguments (removes based on all columns)

Correct Answer: A

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).