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Instant Download Databricks : Associate-Developer-Apache-Spark Questions & Answers as PDF & Test Engine
- Exam Code: Associate-Developer-Apache-Spark
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam
- Updated: Oct 16, 2026
- No. of Questions: 179 Questions and Answers
- Download Limit: Unlimited
Price: $69.98
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In 2026, browser-based tools win on convenience: nothing to install, nothing to configure, and the same experience on Windows, Mac, Android, or iOS. Lead2Passed brings that convenience to Associate-Developer-Apache-Spark preparation with 179 verified practice questions in the online test engine.
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 |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Real Exam Qty: | 45 |
| Exam Duration: | 90 minutes |
| Passing Score: | 70% |
| Exam Price: | USD 200 |
| Exam Format: | Multiple Choice |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Professional Data Engineer |
| Sample Questions: | DOWNLOAD DEMO |
| 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 |
|---|---|---|
| Using Spark Connect and Pandas API on Spark | 10% | - Pandas API Integration - Remote Connectivity |
| Troubleshooting and Tuning | 10% | - Spark UI and Bottlenecks - Debugging Spark Applications - Caching and Shuffle Optimization |
| DataFrame and Dataset API Applications | 30% | - Column Selection and Manipulation - Joins and Transformations - Filtering, Sorting and Aggregation |
| Apache Spark Architecture and Components | 20% | - Partitioning and Fault Tolerance - Driver and Executor Roles - Cluster Mode and Execution Hierarchy |
| Using Spark SQL | 20% | - SQL Queries and Operations - Performance Optimization - Window Functions |
| Structured Streaming | 10% | - Stateful Processing Basics - Watermarks and Triggers - Streaming Concepts |
Associate-Developer-Apache-Spark Preparation: Your Questions Answered
This package includes 179 practice questions for the Databricks Certified Associate Developer for Apache Spark 3.0 exam. Every answer is verified by an experienced team of experts, so you can trust what you study.
Yes. The online test engine runs entirely in a web browser, which is why it works identically on Windows, Mac, Android, and iOS without any installation. If you can open a browser, you can practice — on a computer at home or a phone on the move.
The Databricks Certified Associate Developer for Apache Spark 3.0 exam contains 45 questions, and you will have 90 minutes minutes to complete them. Simulating this exact combination during practice builds both speed and accuracy.
The Databricks Certified Associate Developer for Apache Spark 3.0 exam requires a score of 70% to pass, and the official registration fee is USD 200. Thorough preparation is the most reliable way to protect that investment.
The PC test engine installs on Windows and runs on a standard Java environment, which most computers already have or can add in minutes. Once installed, it simulates the real exam scenario with timed sessions, automatic scoring, and mistake tracking — a full practice lab on your own computer.
The Databricks Certified Associate Developer for Apache Spark 3.0 practice questions are organized around the official exam objectives. Key topics include:
- Apache Spark Architecture and Components (20%)
- Using Spark SQL (20%)
- DataFrame and Dataset API Applications (30%)
Coverage follows the official syllabus, so your practice time always maps to exam content.
No formal prerequisites; recommended hands-on experience with Apache Spark and Databricks environment.
You can confirm the current eligibility requirements on the official Associate-Developer-Apache-Spark exam page before you register.
Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:
Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?
- A. transactionsDf.sort(col("value").asc()).print(10)
- B. transactionsDf.orderBy("value").asc().show(10)
- C. transactionsDf.sort(col("value")).show(10)
- D. transactionsDf.sort(col("value").desc()).head()
- E. transactionsDf.sort(asc(value)).show(10)
Correct Answer: C 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which of the following are valid execution modes?
- A. Cluster, Server, Local
- B. Kubernetes, Local, Client
- C. Client, Cluster, Local
- D. Standalone, Client, Cluster
- E. Server, Standalone, Client
Correct Answer: C 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which of the following code blocks stores a part of the data in DataFrame itemsDf on executors?
- A. itemsDf.rdd.storeCopy()
- B. itemsDf.cache().count()
- C. itemsDf.cache().filter()
- D. cache(itemsDf)
- E. itemsDf.cache(eager=True)
Correct Answer: B 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
The code block displayed below contains an error. The code block should return a DataFrame in which column predErrorAdded contains the results of Python function add_2_if_geq_3 as applied to numeric and nullable column predError in DataFrame transactionsDf. Find the error.
Code block:
1.def add_2_if_geq_3(x):
2. if x is None:
3. return x
4. elif x >= 3:
5. return x+2
6. return x
7.
8.add_2_if_geq_3_udf = udf(add_2_if_geq_3)
9.
10.transactionsDf.withColumnRenamed("predErrorAdded", add_2_if_geq_3_udf(col("predError")))
- A. The udf() method does not declare a return type.
- B. Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError.
- C. The operator used to adding the column does not add column predErrorAdded to the DataFrame.
- D. The Python function is unable to handle null values, resulting in the code block crashing on execution.
- E. UDFs are only available through the SQL API, but not in the Python API as shown in the code block.
Correct Answer: C 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which of the following statements about executors is correct?
- A. Executors are launched by the driver.
- B. Executors store data in memory only.
- C. An executor can serve multiple applications.
- D. Each node hosts a single executor.
- E. Executors stop upon application completion by default.
Correct Answer: E 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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