What is the primary focus of the Databricks Certified Associate Developer for Apache Spark 3.5 exam?

This certification exam validates your ability to develop Apache Spark applications using Python on the Databricks platform. It tests your knowledge of Spark fundamentals, DataFrame operations, SQL queries, and data processing techniques specific to Spark 3.5.

What topics are covered in the certification exam?

The exam covers Apache Spark fundamentals, RDD and DataFrame APIs, Spark SQL, data manipulation and transformation, performance optimization, and working within the Databricks environment. You should also be familiar with Python syntax as it applies to Spark development.

How long is the exam and what is the passing score?

The exam is typically 120 minutes long with multiple-choice and scenario-based questions. Databricks generally requires a score of around 70% or higher to pass, though the exact passing threshold may vary and should be verified on the official Databricks certification page.

What prerequisites or preparation materials are recommended for this exam?

It is recommended to have hands-on experience with Apache Spark and Python, familiarity with the Databricks platform, and understanding of distributed computing concepts. Databricks offers official training courses, documentation, and practice exams to help prepare for the certification.

Is the certification valid indefinitely or does it require renewal?

Databricks certifications are typically valid for a set period, usually 2-3 years, after which renewal or retesting may be required. You should check the official Databricks certification program details to confirm the specific validity period and renewal requirements for this certification.

Free Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Questions

Q1 MultipleChoice

A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.)

Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

Adf.select('Name').orderBy(df['Name'].asc())

Bdf.select('Name').distinct().orderBy(df['Name'])

Cdf.select('Name').distinct()

Ddf.select('Name').distinct().orderBy(df['Name'].desc())

Correct Answer: B

Explanation:

To extract unique values from a column and sort them alphabetically:

distinct() is required to remove duplicate values.

orderBy() is needed to sort the results alphabetically (ascending by default).

Correct code:

df.select('Name').distinct().orderBy(df['Name'])

This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting. Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.

Q2 MultipleChoice

10 of 55.

What is the benefit of using Pandas API on Spark for data transformations?

AIt executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

BIt is available only with Python, thereby reducing the learning curve.

CIt runs on a single node only, utilizing memory efficiently.

DIt computes results immediately using eager execution.

Correct Answer: A

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.

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.

Q3 MultipleChoice

14 of 55.

A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:

df.write.partitionBy("color", "taste").parquet("/path/to/output")

What is the result of this code?

AIt appends new partitions to an existing Parquet file.

BIt throws an error if there are null values in either partition column.

CIt creates separate directories for each unique combination of color and taste.

DIt stores all data in a single Parquet file.

Correct Answer: C

Explanation:

When writing a DataFrame using .partitionBy() in Spark, the data is physically organized into directory structures corresponding to unique combinations of the partition columns.

Example:

/path/to/output/color=Red/taste=Sweet/part-0001.parquet

/path/to/output/color=Green/taste=Sour/part-0002.parquet

This structure improves query performance by pruning partitions when filtering on these columns.

Why the other options are incorrect:

A: Appending requires .mode('append'), which isn't used here.

B: Null values in partition columns are handled; they don't raise errors.

D: Partitioning prevents storing all data in a single file.

PySpark DataFrameWriter API --- partitionBy() and .parquet() methods.

Databricks Exam Guide (June 2025): Section ''Using Spark SQL'' --- partitioning and writing optimized output files.

Q4 MultipleChoice

Given this code:

.withWatermark("event_time", "10 minutes")

.groupBy(window("event_time", "15 minutes"))

.count()

What happens to data that arrives after the watermark threshold?

Options:

ARecords that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.

BAny data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.

CData arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.

DThe watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.

Correct Answer: B

Explanation:

According to Spark's watermarking rules:

''Records that are older than the watermark (event time < current watermark) are considered too late and are dropped.''

So, if a record's event_time is earlier than (max event_time seen so far - 10 minutes), it is discarded.

Q5 MultipleChoice

How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?

Options:

AConfigure the application to run in cluster mode instead of local mode.

BIncrease the number of local threads based on the number of CPU cores.

CUse the spark.dynamicAllocation.enabled property to scale resources dynamically.

DSet the spark.executor.memory property to a large value.

Correct Answer: B

Explanation:

When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.

Using local[*] ensures Spark uses all available CPU cores for parallelism.

Example:

spark-submit --master local[*]

Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.

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