- Exam Code: Associate-Developer-Apache-Spark
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam
- Updated: Aug 31, 2026
- Q & A: 179 Questions and Answers
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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.0 Exam |
| Exam Number: | Associate-Developer-Apache-Spark |
| Available Languages: | English |
| Passing Score: | 70% |
| Exam Format: | Multiple Choice, Multiple Select |
| Related Certifications: | Databricks Certified Associate Data Engineer Databricks Certified Professional Data Engineer |
| Real Exam Qty: | 60 |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Databricks Academy - Apache Spark for Developers Databricks Exam Guide |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | ![]() |
| Exam Way: | Online proctored or in-person at authorized test centers |
| Pre Condition: | Basic programming experience in Python or Scala; working knowledge of SQL; recommended 6+ months of hands-on Spark experience |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 28% | - Lazy evaluation, transformations vs actions - Spark execution model and hierarchy - Driver and executor architecture - Fault tolerance and shuffle operations |
| Topic 2: Developing DataFrame API Applications | 44% | - Handling missing data and duplicates - Column and row manipulation - User-defined functions (UDFs) - Filtering, sorting, aggregation - Reading/writing data and partitioning |
| Topic 3: Troubleshooting and Tuning | 8% | - Query plans and execution analysis - Performance optimization |
| Topic 4: Structured Streaming | 8% | - Streaming concepts and operations - Output modes and triggers |
| Topic 5: Using Spark SQL | 12% | - Working with views and tables - Querying data with Spark SQL - Supported file formats and data sources |
The Databricks Certified Associate Developer for Apache Spark 3.0 is the official Databricks exam that leads to the Databricks Certified Associate Developer for Apache Spark 3.0 certification at the Associate level. Passing it validates your skills against Databricks standards and proves your qualification to current and future employers. The credential is also connected with related certifications such as Databricks Certified Professional Data Engineer, Databricks Certified Associate Data Engineer, so it can serve as a solid step in a broader certification path.
The Associate-Developer-Apache-Spark exam has 60 questions in total and must be completed within 120 minutes. That leaves only a narrow time budget per item, so train yourself to flag a difficult question, move on, and circle back later instead of getting stuck. A week or two before your test date, run at least one full timed mock exam in the Actual4Dumps desktop or online test engine under the same 120 minutes limit, and repeat until you can finish with a few minutes left for review.
The passing score for the Associate-Developer-Apache-Spark exam is 70%, and the official registration fee is $200 USD. Keep in mind that a failed attempt is not discounted — retaking the exam means paying the full fee again — so it is wise not to book your seat until your practice scores sit comfortably above the passing mark. Working through the 179 questions at Actual4Dumps in timed mode is a reliable way to judge when you are truly ready.
Databricks sets the following requirement for the Associate-Developer-Apache-Spark exam: Basic programming experience in Python or Scala; working knowledge of SQL; recommended 6+ months of hands-on Spark experience. Eligibility rules can change from time to time, so always confirm the current prerequisites on the official exam page at https://www.databricks.com/learn/certification/apache-spark-developer-associate before you register.
You can register for the Databricks Certified Associate Developer for Apache Spark 3.0 through the following official channels:
The Associate-Developer-Apache-Spark exam is delivered as Online proctored or in-person at authorized test centers, so review the technical and check-in requirements for that format when you schedule your appointment.
Databricks recommends the following official training options for the Databricks Certified Associate Developer for Apache Spark 3.0:
Official courses build the theory, and pairing them with the 179 practice questions from Actual4Dumps turns that knowledge into exam-ready answers.
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The Databricks Certified Associate Developer for Apache Spark 3.0 is organized into 5 domains. Among the first ones are Developing DataFrame API Applications (44%), Apache Spark Architecture and Components (28%), Structured Streaming (8%), and the remaining domains cover the rest of the official objectives. For the complete topic breakdown with every subtopic, see the full Associate-Developer-Apache-Spark exam outline above on this page.
Question 1
Which of the following code blocks returns a 2-column DataFrame that shows the distinct values in column productId and the number of rows with that productId in DataFrame transactionsDf?
A. transactionsDf.groupBy("productId").select(count("value"))
B. transactionsDf.groupBy("productId").count()
C. transactionsDf.groupBy("productId").agg(col("value").count())
D. transactionsDf.count("productId").distinct()
E. transactionsDf.count("productId")
Question 2
The code block displayed below contains multiple errors. The code block should remove column transactionDate from DataFrame transactionsDf and add a column transactionTimestamp in which dates that are expressed as strings in column transactionDate of DataFrame transactionsDf are converted into unix timestamps. Find the errors.
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+----------------+
2.|transactionId|predError|value|storeId|productId| f| transactionDate|
3.+-------------+---------+-----+-------+---------+----+----------------+
4.| 1| 3| 4| 25| 1|null|2020-04-26 15:35|
5.| 2| 6| 7| 2| 2|null|2020-04-13 22:01|
6.| 3| 3| null| 25| 3|null|2020-04-02 10:53|
7.+-------------+---------+-----+-------+---------+----+----------------+ Code block:
1.transactionsDf = transactionsDf.drop("transactionDate")
2.transactionsDf["transactionTimestamp"] = unix_timestamp("transactionDate", "yyyy-MM-dd")
A. Column transactionDate should be wrapped in a col() operator.
B. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment. Operator to_unixtime() should be used instead of unix_timestamp().
C. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment.
D. The string indicating the date format should be adjusted. The withColumnReplaced operator should be used instead of the drop and assign pattern in the code block to replace column transactionDate with the new column transactionTimestamp.
E. Column transactionDate should be dropped after transactionTimestamp has been written. The withColumn operator should be used instead of the existing column assignment. Column transactionDate should be wrapped in a col() operator.
Question 3
Which of the following is a characteristic of the cluster manager?
A. In client mode, the cluster manager runs on the edge node.
B. Each cluster manager works on a single partition of data.
C. The cluster manager transforms jobs into DAGs.
D. The cluster manager does not exist in standalone mode.
E. The cluster manager receives input from the driver through the SparkContext.
Question 4
Which of the following is the deepest level in Spark's execution hierarchy?
A. Executor
B. Job
C. Stage
D. Task
E. Slot
Question 5
In which order should the code blocks shown below be run in order to create a table of all values in column attributes next to the respective values in column supplier in DataFrame itemsDf?
1. itemsDf.createOrReplaceView("itemsDf")
2. spark.sql("FROM itemsDf SELECT 'supplier', explode('Attributes')")
3. spark.sql("FROM itemsDf SELECT supplier, explode(attributes)")
4. itemsDf.createOrReplaceTempView("itemsDf")
A. 1, 2
B. 1, 3
C. 4, 3
D. 4, 2
E. 2
Solutions:
| Question 1 Answer: B | Question 2 Answer: C | Question 3 Answer: E | Question 4 Answer: D | Question 5 Answer: C |
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