Download Databricks : Associate-Developer-Apache-Spark-3.5 Questions & Answers as PDF & Test Software
Last Updated: Sep 03, 2026
No. of Questions: 135 Questions & Answers with Testing Engine
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Booking the Associate-Developer-Apache-Spark-3.5 exam is an investment, and sitting it twice is an expense nobody plans for. Preparing with TestSimulate's 135 practice questions for Databricks Certified Associate Developer for Apache Spark 3.5 - Python is a practical way to protect that investment before exam day.
Databricks Associate-Developer-Apache-Spark-3.5 Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.5 - Python |
| Exam Number: | Associate-Developer-Apache-Spark-3.5-Python |
| Real Exam Qty: | 45-60 |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Data Engineer Professional |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Exam Format: | Multiple choice, Multiple select |
| Passing Score: | 70% |
| Recommended Training: | Databricks Academy - Apache Spark Training Apache Spark Documentation |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | Basic knowledge of Python programming and SQL is recommended. Familiarity with Apache Spark fundamentals is strongly suggested. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
| Data Processing and Performance | - Optimization techniques - Joins and data partitioning - Caching and persistence strategies |
| Data Ingestion and Storage | - Reading and writing data (Parquet, JSON, CSV) - Delta Lake basics |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| DataFrame API with PySpark | - Built-in functions and expressions - Transformations and actions - DataFrame creation and schema management |
| Apache Spark Fundamentals | - RDD vs DataFrame vs Dataset concepts - Spark architecture and execution model |
Associate-Developer-Apache-Spark-3.5 Exam FAQ: What to Know Before You Book
The Associate-Developer-Apache-Spark-3.5 exam, officially titled Databricks Certified Associate Developer for Apache Spark 3.5 - Python, is the qualifying exam for the Databricks Certified Associate Developer for Apache Spark 3.5 - Python certification, which sits at the Associate level. Earning it shows employers that you have the skills the credential stands for, and it is a solid step forward on a Databricks career path. It also connects to Databricks Certified Data Engineer Associate, Databricks Certified Data Engineer Professional, so the effort you put in now keeps paying off as you advance.
The Associate-Developer-Apache-Spark-3.5 exam gives you 45-60 questions to complete within 90 minutes. Do the math before exam day: divide the total time by the question count to set a steady per-question pace, and flag any item that stalls you so you can return to it after securing the easier points. The most reliable way to build that rhythm is a full timed practice test under the same limit, which is exactly what TestSimulate's test engines are designed for.
The passing score for Databricks Certified Associate Developer for Apache Spark 3.5 - Python is 70%, and the official registration fee is $200 USD. Keep in mind that a retake means paying that fee again in full, which makes an honest self-check worthwhile: before you book, sit a timed practice test and make sure you are consistently scoring above the passing line with some margin to spare.
According to the official requirements: Basic knowledge of Python programming and SQL is recommended. Familiarity with Apache Spark fundamentals is strongly suggested.. Eligibility rules do change from time to time, so confirm the current details on the official exam page before you register.
Registration for the Associate-Developer-Apache-Spark-3.5 exam is handled through the official channels below:
The exam is delivered in the following format: Online proctored exam. Pick a date that leaves room for at least one full timed practice test beforehand.
Databricks recommends the following official training options for candidates preparing for Databricks Certified Associate Developer for Apache Spark 3.5 - Python:
Courses build the foundation, but they work best alongside question practice. That is where TestSimulate comes in: the 135 practice questions in our Associate-Developer-Apache-Spark-3.5 package let you rehearse the exam format at your own pace and see exactly where you stand.
Yes — TestSimulate offers a free PDF demo of the Databricks Certified Associate Developer for Apache Spark 3.5 - Python material, so you can review the question style and answer quality before you spend anything. After purchase, you also receive 365 days of free updates, and if your product expires you can extend the update service at a 50% discount from your member zone.
If you take the corresponding exam within 60 days of your purchase and do not pass, you are covered by the 100% Money Back Guarantee under clear conditions. To claim a full refund, submit a scan of your exam enrollment slip and your official Score Report PDF within two days of the exam; requests are processed within seven days. Note that sitting the exam within three days of purchase is not eligible, purchases that were never followed by an exam sitting do not qualify, free materials and expired orders are excluded, and the candidate name must match the payer name. Prefer an exchange instead? You can swap your product for two free exam packages of equal value and keep your update service. Delivery is immediate: your Associate-Developer-Apache-Spark-3.5 material is ready for instant download and is also emailed to you within one minute of payment — contact customer service if nothing arrives within two hours. There is no limit on the number of computers you can install it on.
The current Databricks Certified Associate Developer for Apache Spark 3.5 - Python syllabus is organized into 6 exam domains. The first three are:
- Structured Streaming Basics
- Data Ingestion and Storage
- Apache Spark Fundamentals
For the full domain and subtopic breakdown, see the Exam Topics section above — that is the outline your TestSimulate practice questions are mapped to.
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question 1
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 ErrorIfExists
B. save with mode Ignore
C. save with mode ErrorIfExists
D. saveAsTable with mode Overwrite
Question 2
19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
What should the signature of the shake_256() function be changed to in order to fix this error?
A. def shake_256(raw: [pd.Series]) -> pd.Series:
B. def shake_256(raw: [str]) -> [str]:
C. def shake_256(raw: str) -> str:
D. def shake_256(raw: pd.Series) -> pd.Series:
Question 3
Given the code fragment:
import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
A. psdf.to_spark()
B. psdf.to_dataframe()
C. psdf.to_pandas()
D. psdf.to_pyspark()
Question 4
11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?
A. spark.sql.shuffle.partitions
B. spark.executor.cores
C. spark.executor.memory
D. spark.task.maxFailures
Question 5
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. 
result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
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 \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
D. 
result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price"))
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: A |
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