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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% | - Creating DataFrames and defining schemas - Selecting, renaming, and modifying columns - User-defined functions (UDFs) - Partitioning and bucketing data - Handling missing values and data quality - Filtering, sorting, and aggregating data - Joining and combining datasets - Reading and writing data in various formats |
| Topic 2: Structured Streaming | 10% | - Fault tolerance and state management - Defining streaming queries - Streaming concepts and architecture - Output modes and triggers |
| Topic 3: Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Overview of Pandas API on Spark - Key differences and limitations |
| Topic 4: Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
| Topic 5: Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Execution and deployment modes - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation - Spark architecture overview |
| Topic 6: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Optimizing transformations and actions - Identifying performance bottlenecks - Managing memory and resource usage |
| Topic 7: Using Spark SQL | 20% | - Working with functions and expressions - Using catalog and metadata APIs - Running SQL queries - Integrating Spark SQL with DataFrames |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?
- A. spark.read.parquet("/data/parquet/")
- B. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
- C. spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
- D. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
Correct Answer: D 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?
- A. It filters the id values before performing the join.
- B. It ensures that the join happens only when the id values are identical.
- C. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
- D. It increases the partition size for df1 and df2.
Correct Answer: C 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?
- A. spark.executor.cores
- B. spark.task.maxFailures
- C. spark.executor.memory
- D. spark.sql.shuffle.partitions
Correct Answer: A 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
Given the following code snippet in my_spark_app.py:
What is the role of the driver node?
- A. The driver node stores the final result after computations are completed by worker nodes
- B. The driver node holds the DataFrame data and performs all computations locally
- C. The driver node only provides the user interface for monitoring the application
- D. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
Correct Answer: D 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?
- A. 'header' 'true'
- B. 'sep' '|'
- C. 'sep' ','
- D. 'header' 'false'
Correct Answer: A 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
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