The Databricks Certified Associate Developer for Apache Spark 3.0 certification has a strong reputation for a reason — the Associate-Developer-Apache-Spark exam tests applied skills, not memorized definitions. Candidates around the world use Actual4Exams practice questions to close knowledge gaps before test day.
Databricks Associate-Developer-Apache-Spark Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.0 Exam |
| Exam Number: | Associate-Developer-Apache-Spark |
| Exam Format: | Multiple select, Multiple choice |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Passing Score: | 70% |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Data Engineer Professional |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45-60 |
| Recommended Training: | Databricks Academy - Apache Spark Fundamentals |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: |  |
| Exam Way: | Online proctored exam |
| Pre Condition: | Recommended 6+ months of experience with Apache Spark and basic Python or Scala knowledge |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Ingestion and Storage Formats | - Reading and writing data
|
| Transformations and Actions | - Core RDD/DataFrame operations
|
| Apache Spark Architecture and Fundamentals | - Spark architecture overview
|
| Spark DataFrame API | - DataFrame operations
|
| Spark SQL | - SQL queries in Spark
|
| Performance and Optimization Basics | - Optimization concepts
|
Databricks Certified Associate Developer for Apache Spark 3.0 (Associate-Developer-Apache-Spark) — Questions Candidates Actually Ask
The Associate-Developer-Apache-Spark exam, officially known as Databricks Certified Associate Developer for Apache Spark 3.0, is the Databricks test that leads to the Databricks Certified Associate Developer for Apache Spark 3.0 certification at the Associate level. Passing it validates the skills employers expect from a certified professional. It is also associated with related credentials such as Databricks Certified Data Engineer Associate, Databricks Certified Data Engineer Professional.
The Associate-Developer-Apache-Spark exam contains 45-60 questions, and you have 120 minutes to complete them. Work out your per-question pace before test day, and flag slow items instead of stalling on them — time pressure, not knowledge, sinks many first attempts. Timed mock exams in the Actual4Exams test engines are the most reliable way to build that rhythm.
The passing score for the Associate-Developer-Apache-Spark exam is 70%, and the official registration fee is $200 USD. If you miss the mark, a retake means paying the full fee again, so book your seat only when you are ready. A practical benchmark: score consistently above the passing line on timed practice tests before scheduling the real exam.
Recommended 6+ months of experience with Apache Spark and basic Python or Scala knowledge
Entry requirements can change, so confirm the latest conditions on the official exam page: https://www.databricks.com/learn/certification.
You can book the Associate-Developer-Apache-Spark exam through the official registration channels below:
Exam delivery: Online proctored exam. Seats at popular test centers fill quickly, so schedule early once your preparation is on track.
Databricks recommends the following training options for Databricks Certified Associate Developer for Apache Spark 3.0 candidates:
Pair any course with the 179 practice questions from Actual4Exams to measure how ready you really are before paying the exam fee.
Yes. A free PDF demo of the Databricks Certified Associate Developer for Apache Spark 3.0 questions is available, so you can check the question style and answer quality before you pay. Every purchase also includes 365 days of free updates, and if the product expires you can renew the update service at a 50% discount from your member zone.
If you take the corresponding Associate-Developer-Apache-Spark exam within 60 days of purchase and do not pass, you can apply for a full refund under the 100% Money Back Guarantee: submit a scan of your enrollment slip and your official Score Report (PDF) within 2 days of the exam date, and the claim is processed within 7 days. Attempts made within 3 days of purchase, downloads without an actual exam attempt, free materials, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your purchase for two free products of equal value and keep the update service on your original product. As for delivery, the files are available for instant download and are also emailed to you within one minute of payment — if nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you can install the product on.
The official Databricks Certified Associate Developer for Apache Spark 3.0 outline is organized into 6 domains. The first three are:
- Performance and Optimization Basics
- Transformations and Actions
- Spark SQL
See the complete exam topics section above for the full outline and the weighting of every domain.
Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:
Question 1
Which of the following describes the characteristics of accumulators?
A. If an action including an accumulator fails during execution and Spark manages to restart the action and complete it successfully, only the successful attempt will be counted in the accumulator.
B. Accumulators are immutable.
C. Accumulators can be instantiated directly via the accumulator(n) method of the pyspark.RDD module.
D. Accumulators are used to pass around lookup tables across the cluster.
E. All accumulators used in a Spark application are listed in the Spark UI.
Question 2
Which of the following statements about reducing out-of-memory errors is incorrect?
A. Reducing partition size can help against out-of-memory errors.
B. Concatenating multiple string columns into a single column may guard against out-of-memory errors.
C. Limiting the amount of data being automatically broadcast in joins can help against out-of-memory errors.
D. Decreasing the number of cores available to each executor can help against out-of-memory errors.
E. Setting a limit on the maximum size of serialized data returned to the driver may help prevent out-of-memory errors.
Question 3
Which of the following statements about Spark's execution hierarchy is correct?
A. In Spark's execution hierarchy, a job may reach over multiple stage boundaries.
B. In Spark's execution hierarchy, executors are the smallest unit.
C. In Spark's execution hierarchy, a stage comprises multiple jobs.
D. In Spark's execution hierarchy, tasks are one layer above slots.
E. In Spark's execution hierarchy, manifests are one layer above jobs.
Question 4
Which of the following code blocks performs a join in which the small DataFrame transactionsDf is sent to all executors where it is joined with DataFrame itemsDf on columns storeId and itemId, respectively?
A. itemsDf.merge(transactionsDf, "itemsDf.itemId == transactionsDf.storeId", "broadcast")
B. itemsDf.join(broadcast(transactionsDf), itemsDf.itemId == transactionsDf.storeId)
C. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "right_outer")
D. itemsDf.join(transactionsDf, broadcast(itemsDf.itemId == transactionsDf.storeId))
E. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "broadcast")
Question 5
The code block shown below should return a column that indicates through boolean variables whether rows in DataFrame transactionsDf have values greater or equal to 20 and smaller or equal to
30 in column storeId and have the value 2 in column productId. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__((__2__.__3__) __4__ (__5__))
A. 1. select
2. col("storeId")
3. between(20, 30)
4. &
5. col("productId")==2
B. 1. select
2. "storeId"
3. between(20, 30)
4. &&
5. col("productId")==2
C. 1. select
2. col("storeId")
3. between(20, 30)
4. &&
5. col("productId")=2
D. 1. select
2. col("storeId")
3. between(20, 30)
4. and
5. col("productId")==2
E. 1. where
2. col("storeId")
3. geq(20).leq(30)
4. &
5. col("productId")==2
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: C |
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