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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 |
| Passing Score: | 70% |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Data Engineer Professional |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Exam Price: | $200 USD |
| Exam Format: | Multiple select, Multiple choice |
| Real Exam Qty: | 45-60 |
| Recommended Training: | Databricks Academy - Apache Spark Training Apache Spark Documentation |
| Exam Registration: | Databricks Certification Portal |
| 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 |
|---|---|
| Topic 1: Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| Topic 2: Structured Streaming Basics | - Windowed aggregations in streaming - Streaming DataFrames |
| Topic 3: DataFrame API with PySpark | - Built-in functions and expressions - DataFrame creation and schema management - Transformations and actions |
| Topic 4: Data Ingestion and Storage | - Reading and writing data (Parquet, JSON, CSV) - Delta Lake basics |
| Topic 5: Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Topic 6: Data Processing and Performance | - Caching and persistence strategies - Optimization techniques - Joins and data partitioning |
Associate-Developer-Apache-Spark-3.5 Exam FAQ
Registration for the Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam costs $200 USD, and passing requires 70%. Retakes cost the full fee again, so gauge your readiness with the ExamCost practice engines before booking your seat.
The written refund policy applies. Take the Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam within 60 days of purchase, and if you do not pass, ExamCost refunds you in full. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Alternatively, exchange for two free exam products of equal value and keep your original update service. Orders arrive by email within 1 minute — contact support if nothing arrives within 2 hours.
Online proctored exam Registration for the Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam is handled through these official channels:
After booking, your ExamCost practice questions arrive by email within 1 minute — preparation can begin the same day.
The Associate-Developer-Apache-Spark-3.5 exam is a Databricks certification exam validating the skills in the Databricks Certified Associate Developer for Apache Spark 3.5 - Python syllabus shown above. It counts toward these credential paths: Databricks Certified Data Engineer Associate, Databricks Certified Data Engineer Professional. Candidates at many career stages take it in 2026 to demonstrate verified capability. ExamCost prepares you with 135 practice questions in PDF, Desktop Test Engine, and Online Test Engine formats.
Everything, in one purchase: 135 practice questions for the Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam in three formats — a printable, expert-prepared PDF with instant download; a Desktop Test Engine for Windows that simulates the real exam with two practice modes and works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. You also receive a free demo, 365 days of free updates from a team that keeps innovating on the material, a 50% renewal discount afterward, unlimited installations, and considerate pre-sale and after-sale service.
The Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam contains 45-60 with 90 minutes to complete them. Practicing under the same time constraint with the ExamCost Desktop Test Engine makes exam-day pacing feel rehearsed.
Basic knowledge of Python programming and SQL is recommended. Familiarity with Apache Spark fundamentals is strongly suggested. Since policies evolve, confirm the current requirements on the official Databricks exam page before you schedule.
Databricks recommends the following training for the Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam:
Supplement the training with the 135 practice questions from ExamCost — self-testing reveals what training alone cannot.
The official Databricks Certified Associate Developer for Apache Spark 3.5 - Python outline defines 6 domains, the three largest being Spark SQL, DataFrame API with PySpark, and Data Processing and Performance. The complete list is in the topics section above, and ExamCost's 135 practice questions address every domain.
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?
- A. employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")
- B. employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
- C. employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)
- D. employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")
Correct Answer: B 🗳️
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
Which UDF implementation calculates the length of strings in a Spark DataFrame?
- A. df.withColumn("length", udf(lambda s: len(s), StringType()))
- B. df.withColumn("length", spark.udf("len", StringType()))
- C. spark.udf.register("stringLength", lambda s: len(s))
- D. df.select(length(col("stringColumn")).alias("length"))
Correct Answer: D 🗳️
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?
- A. Shuffle join because no broadcast hints were provided.
- B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
- C. Broadcast join, as df2 is smaller than the default broadcast threshold.
- D. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
Correct Answer: C 🗳️
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
- A. A job contains multiple tasks, and each task contains multiple stages.
- B. A stage contains multiple tasks, and each task contains multiple jobs.
- C. A job contains multiple stages, and each stage contains multiple tasks.
- D. A stage contains multiple jobs, and each job contains multiple tasks.
Correct Answer: C 🗳️
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?
- A. Collecting persistent table statistics and storing them in the metastore for future use
- B. Optimizing the layout of Delta files on disk
- C. Dynamically switching join strategies
- D. Improving the performance of single-stage Spark jobs
Correct Answer: C 🗳️
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