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Exam blueprints do not stand still, and neither does ActualVCE: the Associate-Developer-Apache-Spark materials are revised whenever Databricks shifts its objectives, and your purchase includes 365 days of free updates through 2026 and beyond. Your 179 practice questions never go stale.
Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting and Tuning | 8% | - Performance optimization - Query plans and execution analysis |
| Using Spark SQL | 12% | - Working with views and tables - Querying data with Spark SQL - Supported file formats and data sources |
| Developing DataFrame API Applications | 44% | - Column and row manipulation - Filtering, sorting, aggregation - User-defined functions (UDFs) - Reading/writing data and partitioning - Handling missing data and duplicates |
| Apache Spark Architecture and Components | 28% | - Fault tolerance and shuffle operations - Spark execution model and hierarchy - Lazy evaluation, transformations vs actions - Driver and executor architecture |
| Structured Streaming | 8% | - Output modes and triggers - Streaming concepts and operations |
Common Questions About Preparing for the Associate-Developer-Apache-Spark Exam
Registration for the Databricks Certified Associate Developer for Apache Spark 3.0 goes through the official channels listed here:
As for the format, the Associate-Developer-Apache-Spark exam is delivered Online proctored or in-person at authorized test centers — pick whichever fits your situation when you schedule.
The official Databricks Certified Associate Developer for Apache Spark 3.0 blueprint is divided into 5 domains, led by Using Spark SQL (12%), Developing DataFrame API Applications (44%), and Troubleshooting and Tuning (8%). The full domain-by-domain breakdown is in the syllabus section above — that is the checklist your study plan should follow.
The passing score for the Associate-Developer-Apache-Spark exam is 70%, and registering officially costs $200 USD. That fee buys exactly one attempt — if you fall short, the retake is charged in full again, no discount for a second try. The practical move is to measure yourself first: drill the 179 practice questions at ActualVCE under timed conditions until your mock scores clear the bar with room to spare, then book your seat.
You can. ActualVCE publishes a free PDF demo of the Associate-Developer-Apache-Spark practice questions — download it, work through it, and decide on evidence rather than promises. Once you own the full product, 365 days of free updates are included, and an expired product can have its update service renewed at a 50% discount from your member zone.
The Associate-Developer-Apache-Spark exam — officially the Databricks Certified Associate Developer for Apache Spark 3.0 Exam — is how Databricks verifies that you can apply its technologies in real working scenarios, and passing it earns you the Databricks Certified Associate Developer for Apache Spark 3.0 certification, a credential at the Associate level. Employers recognize it because it is vendor-issued and skills-based. Candidates often continue toward related credentials such as Databricks Certified Professional Data Engineer, Databricks Certified Associate Data Engineer once this one is in hand.
Yes — Databricks lists these training options for the Databricks Certified Associate Developer for Apache Spark 3.0:
Courseware teaches the concepts; repetition makes them exam-proof. Combine whichever course you choose with the 179 practice questions from ActualVCE and you cover both halves of preparation.
You will face 60 questions within 120 minutes on the Associate-Developer-Apache-Spark exam. Do the math once and the lesson is clear: dwell too long on a hard item and the clock eats your easy points later. The fix is rehearsal, not luck — take full-length timed mocks in the ActualVCE desktop or online test engine until pacing becomes automatic, and learn to flag a question, move on, and return with fresh eyes.
First, delivery: your download is available immediately after payment, and the package also arrives in your email within one minute. If 2 hours pass with nothing in your inbox, check spam and contact support — and feel free to install the software on as many computers as you like, there is no device limit.
Second, the safety net: if you sit the Associate-Developer-Apache-Spark exam within 60 days of purchase and do not pass, the ActualVCE money back guarantee entitles you to a full refund. File the claim within 2 days of the exam with a scanned enrollment slip and your official score report (PDF); claims are processed within 7 days. The candidate name must match the payer name, and the guarantee excludes exams taken within 3 days of purchase, purchases never used in an actual exam attempt, free materials, and expired orders. If you would rather keep studying, you can swap the product for two free exam packages of equal value and keep the update service on your original purchase.
Basic programming experience in Python or Scala; working knowledge of SQL; recommended 6+ months of hands-on Spark experience
Vendor policies shift from time to time, so treat this as your starting point and verify the current criteria on the official Databricks exam page before you schedule anything.
Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:
The code block shown below should set the number of partitions that Spark uses when shuffling data for joins or aggregations to 100. Choose the answer that correctly fills the blanks in the code block to accomplish this.
spark.sql.shuffle.partitions
__1__.__2__.__3__(__4__, 100)
- A. 1. pyspark
2. config
3. set
4. "spark.sql.shuffle.partitions" - B. 1. pyspark
2. config
3. set
4. spark.shuffle.partitions - C. 1. spark
2. conf
3. set
4. "spark.sql.aggregate.partitions" - D. 1. spark
2. conf
3. set
4. "spark.sql.shuffle.partitions" - E. 1. spark
2. conf
3. get
4. "spark.sql.shuffle.partitions"
Correct Answer: D 🗳️
Explanation: Only visible for ActualVCE members. You can sign-up / login (it's free).
The code block shown below should add column transactionDateForm to DataFrame transactionsDf. The column should express the unix-format timestamps in column transactionDate as string type like Apr 26 (Sunday). Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, from_unixtime(__3__, __4__))
- A. 1. withColumn
2. "transactionDateForm"
3. "MMM d (EEEE)"
4. "transactionDate" - B. 1. withColumnRenamed
2. "transactionDate"
3. "transactionDateForm"
4. "MM d (EEE)" - C. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MM d (EEE)" - D. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MMM d (EEEE)" - E. 1. select
2. "transactionDate"
3. "transactionDateForm"
4. "MMM d (EEEE)"
Correct Answer: D 🗳️
Explanation: Only visible for ActualVCE members. You can sign-up / login (it's free).
The code block shown below should return the number of columns in the CSV file stored at location filePath.
From the CSV file, only lines should be read that do not start with a # character. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
__1__(__2__.__3__.csv(filePath, __4__).__5__)
- A. 1. size
2. spark
3. read()
4. escape='#'
5. columns - B. 1. size
2. pyspark
3. DataFrameReader
4. comment='#'
5. columns - C. 1. len
2. spark
3. read
4. comment='#'
5. columns - D. 1. len
2. pyspark
3. DataFrameReader
4. comment='#'
5. columns - E. 1. DataFrame
2. spark
3. read()
4. escape='#'
5. shape[0]
Correct Answer: C 🗳️
Explanation: Only visible for ActualVCE members. You can sign-up / login (it's free).
Which of the following statements about Spark's configuration properties is incorrect?
- A. The default number of partitions to use when shuffling data for joins or aggregations is 300.
- B. The maximum number of tasks that an executor can process at the same time is controlled by the spark.executor.cores property.
- C. The maximum number of tasks that an executor can process at the same time is controlled by the spark.task.cpus property.
- D. The default number of partitions returned from certain transformations can be controlled by the spark.default.parallelism property.
- E. The default value for spark.sql.autoBroadcastJoinThreshold is 10MB.
Correct Answer: A 🗳️
Explanation: Only visible for ActualVCE members. You can sign-up / login (it's free).
Which of the following describes a narrow transformation?
- A. narrow transformation is an operation in which data is exchanged across partitions.
- B. A narrow transformation is a process in which data from multiple RDDs is used.
- C. A narrow transformation is an operation in which no data is exchanged across the cluster.
- D. A narrow transformation is a process in which 32-bit float variables are cast to smaller float variables, like 16-bit or 8-bit float variables.
- E. A narrow transformation is an operation in which data is exchanged across the cluster.
Correct Answer: C 🗳️
Explanation: Only visible for ActualVCE members. You can sign-up / login (it's free).
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