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Databricks Associate-Developer-Apache-Spark : Databricks Certified Associate Developer for Apache Spark 3.0 Exam
Exam Code: Associate-Developer-Apache-Spark
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam
Updated: Oct 22, 2026
Q & A: 179 Questions and Answers
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Databricks Associate-Developer-Apache-Spark Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark |
| Exam Number: | Databricks Certified Associate Developer for Apache Spark |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45 |
| Related Certifications: | Databricks Certified Professional Data Engineer Databricks Certified Data Engineer Associate |
| Passing Score: | 70% |
| Exam Price: | USD 200 |
| Sample Questions: |  |
| Exam Way: | Online proctored via Databricks/Testing partner or at authorized test centers |
| Pre Condition: | No formal prerequisites; recommended hands-on experience with Apache Spark and Databricks environment. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| DataFrame and Dataset API Applications | 30% | - Filtering, Sorting and Aggregation - Joins and Transformations - Column Selection and Manipulation |
| Structured Streaming | 10% | - Stateful Processing Basics - Watermarks and Triggers - Streaming Concepts |
| Using Spark Connect and Pandas API on Spark | 10% | - Remote Connectivity - Pandas API Integration |
| Troubleshooting and Tuning | 10% | - Caching and Shuffle Optimization - Spark UI and Bottlenecks - Debugging Spark Applications |
| Using Spark SQL | 20% | - SQL Queries and Operations - Window Functions - Performance Optimization |
| Apache Spark Architecture and Components | 20% | - Driver and Executor Roles - Cluster Mode and Execution Hierarchy - Partitioning and Fault Tolerance |
Databricks Certified Associate Developer for Apache Spark 3.0 Exam: Three Formats, Full Support
The official Databricks Certified Associate Developer for Apache Spark 3.0 outline defines 6 domains. The leading three are Troubleshooting and Tuning (10%), DataFrame and Dataset API Applications (30%), and Using Spark SQL (20%). The full list is in the topics section above, and Pass4cram's 179 practice questions cover each one.
No formal prerequisites; recommended hands-on experience with Apache Spark and Databricks environment. Since requirements change over time, confirm the latest on the official Databricks exam page before scheduling.
Online proctored via Databricks/Testing partner or at authorized test centers Register for the Databricks Certified Associate Developer for Apache Spark 3.0 exam through these official channels:
After booking, Pass4cram delivers your practice questions within 1 minute — preparation starts the same day.
The Associate-Developer-Apache-Spark exam is a Databricks certification exam validating the Databricks Certified Associate Developer for Apache Spark 3.0 syllabus shown above. It belongs to these credential paths: Databricks Certified Professional Data Engineer, Databricks Certified Data Engineer Associate. Candidates in 2026 start with Pass4cram's 179 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
The Databricks Certified Associate Developer for Apache Spark 3.0 exam contains 45 to complete within 90 minutes. Timed simulation in the Pass4cram Desktop Test Engine teaches that rhythm before exam day does.
Pass4cram issues a full refund if you take the Databricks Certified Associate Developer for Apache Spark 3.0 exam within 60 days of purchase and do not pass. 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.
The Databricks Certified Associate Developer for Apache Spark 3.0 exam requires 70% to pass, and registration costs USD 200. A failed attempt means paying the same fee again — a readiness check with the Pass4cram engines before booking is the cheaper option.
Databricks recommends these training resources for the Databricks Certified Associate Developer for Apache Spark 3.0 exam:
Follow the training with steady self-testing — the 179 practice questions from Pass4cram help you apply theory and fill any gaps the training leaves.
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 179 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:
Question #1
The code block shown below should write DataFrame transactionsDf to disk at path csvPath as a single CSV file, using tabs (\t characters) as separators between columns, expressing missing values as string n/a, and omitting a header row with column names. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.write.__2__(__3__, " ").__4__.__5__(csvPath)
- A. 1. repartition(1)
2. option
3. "sep"
4. option("nullValue", "n/a")
5. csv
(Correct) - B. 1. csv
2. option
3. "sep"
4. option("emptyValue", "n/a")
5. path
* 1. repartition(1)
2. mode
3. "sep"
4. mode("nullValue", "n/a")
5. csv - C. 1. coalesce(1)
2. option
3. "colsep"
4. option("nullValue", "n/a")
5. path - D. 1. coalesce(1)
2. option
3. "sep"
4. option("header", True)
5. path
Correct Answer: A 🗳️
Explanation: Only visible for Pass4cram members. You can sign-up / login (it's free).
Question #2
The code block shown below should return a one-column DataFrame where the column storeId is converted to string type. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__.__3__(__4__))
- A. 1. select
2. storeId
3. cast
4. StringType() - B. 1. cast
2. "storeId"
3. as
4. StringType() - C. 1. select
2. col("storeId")
3. cast
4. StringType() - D. 1. select
2. col("storeId")
3. cast
4. StringType - E. 1. select
2. col("storeId")
3. as
4. StringType
Correct Answer: C 🗳️
Explanation: Only visible for Pass4cram members. You can sign-up / login (it's free).
Question #3
Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.+-------------+---------+-----+-------+---------+----+
- A. transactionsDf.drop([col("predError"), col("value")])
- B. transactionsDf.drop("predError", "value")
- C. transactionsDf.drop(["predError", "value"])
- D. transactionsDf.drop(value, predError)
- E. transactionsDf.drop(col("value"), col("predError"))
Correct Answer: B 🗳️
Explanation: Only visible for Pass4cram members. You can sign-up / login (it's free).
Question #4
Which of the following code blocks prints out in how many rows the expression Inc. appears in the string-type column supplier of DataFrame itemsDf?
- A. print(itemsDf.foreach(lambda x: 'Inc.' in x).sum())
- B. 1.accum=sc.accumulator(0)
2.
3.def check_if_inc_in_supplier(row):
4. if 'Inc.' in row['supplier']:
5. accum.add(1)
6.
7.itemsDf.foreach(check_if_inc_in_supplier)
8.print(accum.value) - C. 1.counter = 0
2.
3.for index, row in itemsDf.iterrows():
4. if 'Inc.' in row['supplier']:
5. counter = counter + 1
6.
7.print(counter) - D. 1.counter = 0
2.
3.def count(x):
4. if 'Inc.' in x['supplier']:
5. counter = counter + 1
6.
7.itemsDf.foreach(count)
8.print(counter) - E. print(itemsDf.foreach(lambda x: 'Inc.' in x))
Correct Answer: B 🗳️
Explanation: Only visible for Pass4cram members. You can sign-up / login (it's free).
Question #5
Which of the following code blocks returns a single-row DataFrame that only has a column corr which shows the Pearson correlation coefficient between columns predError and value in DataFrame transactionsDf?
- A. transactionsDf.select(corr(col("predError"), col("value")).alias("corr")).first()
- B. transactionsDf.select(corr(["predError", "value"]).alias("corr")).first()
- C. transactionsDf.select(corr(col("predError"), col("value")).alias("corr")) (Correct)
- D. transactionsDf.select(corr("predError", "value"))
- E. transactionsDf.select(corr(predError, value).alias("corr"))
Correct Answer: C 🗳️
Explanation: Only visible for Pass4cram members. You can sign-up / login (it's free).
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