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Last Updated: Oct 15, 2026
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.5 - Python |
| Exam Number: | Associate-Developer-Apache-Spark-3.5 |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Exam Format: | Multiple Choice |
| Passing Score: | 70% |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45 |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Databricks Academy Training |
| Exam Registration: | Databricks Certification Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or onsite proctored |
| Pre Condition: | No formal prerequisites; recommended 6+ months hands-on experience with PySpark and DataFrame API |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Overview of Pandas API on Spark - Key differences and limitations |
| Topic 2: Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Spark architecture overview - Execution hierarchy and lazy evaluation - Execution and deployment modes - Fault tolerance and garbage collection |
| Topic 3: Developing Apache Spark DataFrame API Applications | 30% | - Reading and writing data in various formats - Selecting, renaming, and modifying columns - Filtering, sorting, and aggregating data - Partitioning and bucketing data - Joining and combining datasets - Creating DataFrames and defining schemas - User-defined functions (UDFs) - Handling missing values and data quality |
| Topic 4: Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Spark Connect architecture - Connecting to remote Spark clusters |
| Topic 5: Using Spark SQL | 20% | - Working with functions and expressions - Integrating Spark SQL with DataFrames - Running SQL queries - Using catalog and metadata APIs |
| Topic 6: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Managing memory and resource usage - Debugging and logging - Identifying performance bottlenecks |
| Topic 7: Structured Streaming | 10% | - Output modes and triggers - Streaming concepts and architecture - Defining streaming queries - Fault tolerance and state management |
Associate-Developer-Apache-Spark-3.5 Exam FAQ — Valid Answers
No formal prerequisites; recommended 6+ months hands-on experience with PySpark and DataFrame API Vendors revise eligibility rules periodically, so confirm the current requirements on the official page (official Associate-Developer-Apache-Spark-3.5 exam page) before registering.
Delivery is instant — an automatic email within a minute of payment, unlimited devices, and 7*24 online service (replies within two hours) if anything's missing after 2 hours; check spam first. If you fail the corresponding Associate-Developer-Apache-Spark-3.5 exam within 60 days of purchase, we refund all the cost you paid: send a scanned enrollment slip plus the official Score Report PDF within 2 days of the exam, processed within 7 days. Excluded: exams within 3 days of purchase, candidate names that don't match the payer, and free or expired products. Alternatively, exchange for two equal-value products free.
90 minutes for 45 questions. Train the pace in advance: timed practice sessions turn the exam clock from a threat into a habit.
Yes:
Official courses build the base; the 135 practice questions for the Databricks Certified Associate Developer for Apache Spark 3.5 - Python — every answer expert-verified — build the readiness.
The Databricks Certified Associate Developer for Apache Spark 3.5 - Python blueprint spans 7 domains — including Troubleshooting and Tuning Apache Spark DataFrame API Applications (10%), Using Spark SQL (20%), Using Spark Connect to Deploy Applications (5%). Study in proportion to the weightings; the complete outline above details every subtopic.
Yes — free demo downloads let you tell whether the Databricks Certified Associate Developer for Apache Spark 3.5 - Python material suits you before purchasing. Purchases include 365 days of free updates, each new version emailed immediately; renew afterward at 50% off.
Through the vendor's official registration channels:
The Databricks Certified Associate Developer for Apache Spark 3.5 - Python is delivered Online proctored or onsite proctored — choose the arrangement that fits you when booking.
$200 USD per attempt, 70% to pass. Retakes bill again at full price — prepare thoroughly with the 135 practice questions for the Associate-Developer-Apache-Spark-3.5 exam at BraindumpQuiz first.
The Databricks Certified Associate Developer for Apache Spark 3.5 - Python is Databricks's certification exam for Databricks Certified Associate Developer for Apache Spark 3.5 - Python, at the Associate level. These certifications are valued precisely because they're demanding — passing one validates real capability.
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:
Which of the following code snippets will read all the data within the directory structure?
- A. df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
- B. df = spark.read.parquet("/path/events/data/*")
- C. df = spark.read.parquet("/path/events/data/")
- D. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
Correct Answer: D 🗳️
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A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
- A. By configuring the option recoveryLocation during the SparkSession initialization
- B. By configuring the option recoveryLocation during writeStream
- C. By configuring the option checkpointLocation during readStream
- D. By configuring the option checkpointLocation during writeStream
Correct Answer: D 🗳️
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A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)
- A. Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
- B. Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
- C. Execute their pyspark shell with the option --remote "sc://localhost"
- D. Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
- E. Execute their pyspark shell with the option --remote "https://localhost"
Correct Answer: C,D 🗳️
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5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
- A. A stage contains multiple tasks, and each task contains multiple jobs.
- B. A job contains multiple stages, and each stage contains multiple tasks.
- C. A stage contains multiple jobs, and each job contains multiple tasks.
- D. A job contains multiple tasks, and each task contains multiple stages.
Correct Answer: B 🗳️
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14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?
- A. It creates separate directories for each unique combination of color and taste.
- B. It appends new partitions to an existing Parquet file.
- C. It stores all data in a single Parquet file.
- D. It throws an error if there are null values in either partition column.
Correct Answer: A 🗳️
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