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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Creating DataFrames and defining schemas - Partitioning and bucketing data - Filtering, sorting, and aggregating data - Reading and writing data in various formats - Selecting, renaming, and modifying columns - Handling missing values and data quality - User-defined functions (UDFs) |
| Topic 2: Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Converting between Pandas and Spark structures - Key differences and limitations |
| Topic 3: Apache Spark Architecture and Components | 20% | - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation - Execution and deployment modes - Shuffling, actions, and broadcasting - Spark architecture overview |
| Topic 4: Structured Streaming | 10% | - Defining streaming queries - Output modes and triggers - Fault tolerance and state management - Streaming concepts and architecture |
| Topic 5: Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs - Running SQL queries - Working with functions and expressions |
| Topic 6: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Identifying performance bottlenecks - Managing memory and resource usage - Debugging and logging - Optimizing transformations and actions |
| Topic 7: Using Spark Connect to Deploy Applications | 5% | - Connecting to remote Spark clusters - Spark Connect architecture - Running applications via Spark Connect |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?
- A. 40
- B. 80
- C. 160
- D. 64
Correct Answer: B 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?
- A. df.write.mode("append").json("path/to/file")
- B. df.write.option("overwrite").json("path/to/file")
- C. df.write.json("path/to/file")
- D. df.write.mode("overwrite").json("path/to/file")
Correct Answer: D 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?
- A. By configuring the option recoveryLocation during writeStream.
- B. By configuring the option checkpointLocation during readStream.
- C. By configuring the option checkpointLocation during writeStream.
- D. By configuring the option recoveryLocation during SparkSession initialization.
Correct Answer: C 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?
- A. saveAsTable with mode Overwrite
- B. save with mode Ignore
- C. save with mode ErrorIfExists
- D. saveAsTable with mode ErrorIfExists
Correct Answer: D 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?
- A. spark.read.orc("/file/test_data.orc").selected("col1", "col2")
- B. spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
- C. spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
- D. spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
Correct Answer: D 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
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