Spark
What PySpark interview questions should I practise?
Updated
PySpark questions follow the published Data Engineering syllabus. Apache Spark and PySpark sit in the middle of the six months. Explain a job, a stage, and a shuffle, and walk through a pipeline you built. Kafka and streaming stay self-study. There is no secret question bank, and no promise an interviewer asks this list.
What the syllabus actually names
Apache Spark and PySpark are in the published middle of the Data Engineering course, with AWS and Databricks. The three published projects include a batch pipeline and a medallion lakehouse. You should be able to explain work you built.
Kafka and Spark Structured Streaming are not core modules. They are self-study for the day a job description asks for streaming.
What to practise
Explain a Spark job, a stage, and a shuffle in plain language. Say what your pipeline read, how it transformed the data, and where it wrote the result. An interviewer is allowed to ask who owned production. Answer that honestly.
The module list is the Data Engineering course. The Spark skill note is Spark.
Questions
Asked before you book.
Is there a published PySpark question bank?
No. Practise the published syllabus: Spark, PySpark, and the projects you can defend. We will not pretend a secret list is the interview.
Are Kafka and streaming on the core syllabus?
No. They are self-study. If a brochure adds them as week-one modules, ask us to put the correction in writing.
Does practising these questions guarantee an interview?
No. Nobody can guarantee an interview or a job.
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