Databricks Certified Associate Developer for Apache Spark (Python) Practice Test
Build your confidence for Databricks Certified Associate Developer for Apache Spark (Python). Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
Try a sample questionExam overview and details
The Databricks Certified Associate Developer for Apache Spark Python exam validates a candidate's ability to use the Spark DataFrame API and PySpark to perform data engineering tasks. This 36-question assessment tests practical knowledge of core Spark architecture, DataFrame operations, and Python-specific implementations within the Spark ecosystem. It is designed for data engineers, data scientists, and developers who build and maintain production-level data processing applications using PySpark. Successful candidates demonstrate proficiency in writing efficient, scalable Spark code for data transformation, aggregation, and analysis. Passing this exam signifies a foundational, hands-on competency in applying Spark's distributed computing principles to solve real-world data problems, making it a recognized credential for professionals working with big data pipelines.
Sample Questions
Choose an answer and explore the explanation to see how practice works.
A developer needs to write DataFrame orders to a Parquet path, overwrite existing data, and physically partition the output by country for later pruning. Which PySpark write pattern is correct?
A claims enrichment pipeline groups 420 GB by policy_id. The team changed spark.sql.shuffle.partitions from 200 to 24 to reduce small files, and now several reducers run much longer than the rest. What does this setting control?
A team creates a persistent Spark SQL table for orders and frequently filters by country while ordering recent rows by ingestion time inside each partition. Which approach best supports retrieval without changing query semantics?
A notebook creates two SparkSession objects for a claims enrichment experiment, each with a different warehouse directory. The second object appears to reuse the same application and catalog state. Which explanation is most accurate?
A CSV-based orders ingestion job uses Spark SQL and occasionally receives rows with embedded delimiters and malformed records. Which direction is most defensible before querying the data?
Career Opportunities & Salary
Exam insights and study advice
In the real world, inefficient or incorrect Spark code leads to significant performance bottlenecks, inflated cloud costs, and unreliable data pipelines. This certification matters because it proves you can write code that scales efficiently across clusters, manages memory and resources effectively, and produces correct results. It moves beyond theoretical understanding to validate the practical skills needed to build maintainable, production-ready data applications, directly impacting an organization's data infrastructure reliability and operational efficiency.
What this exam covers
Use the published domain weights to plan your study. Practice results do not predict your certification exam score.