Quick Answer
The answer is the DataflowCreatePythonJobOperator. This operator is the correct choice because it is purpose-built to submit and manage Apache Beam pipelines written in Python as Dataflow jobs within a Cloud Composer DAG, directly handling the creation of a Dataflow job from a Python file for the transform step. On the Google Professional Data Engineer exam, this question tests your understanding of how Airflow operators map to specific Google Cloud services, often appearing in scenario-based questions about automating batch data processing pipelines that extract from Cloud Storage, transform with Dataflow, and load into BigQuery. A common trap is confusing this with the DataflowJavaOperator or the generic DataflowTemplateOperator, but the key distinction is that DataflowCreatePythonJobOperator is specifically for Python-based Beam pipelines, not Java or pre-built templates. Memory tip: think “Python job” for “Python file” — if your Dataflow code is a .py script, this is the operator you need.
This PDE practice question tests your understanding of designing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An organization wants to automate their batch data processing pipeline using Cloud Composer. The pipeline consists of multiple tasks: extract from Cloud Storage, transform with Dataflow, and load into BigQuery. Which Airflow operator should be used to run Dataflow jobs?
BigQueryInsertJobOperator
Why wrong: This runs queries, not Dataflow jobs.
DataflowCreatePythonJobOperator
This operator submits a Dataflow job written in Python.
GCSToBigQueryOperator
Why wrong: This loads from GCS to BigQuery directly, no transform.
DataprocSubmitJobOperator
Why wrong: This is for Dataproc jobs, not Dataflow.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
DataflowCreatePythonJobOperator
B is correct because the DataflowCreatePythonJobOperator is specifically designed to submit and manage Apache Beam pipelines written in Python as Dataflow jobs in Google Cloud. This operator handles the creation of a Dataflow job from a Python file, which aligns with the requirement to run Dataflow transformations within a Cloud Composer DAG.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
BigQueryInsertJobOperator
Why it's wrong here
This runs queries, not Dataflow jobs.
DataflowCreatePythonJobOperator
Why this is correct
This operator submits a Dataflow job written in Python.
Related concept
Read the scenario before looking for a memorised answer.
GCSToBigQueryOperator
Why it's wrong here
This loads from GCS to BigQuery directly, no transform.
DataprocSubmitJobOperator
Why it's wrong here
This is for Dataproc jobs, not Dataflow.
Common exam traps
Google Cloud often tests the distinction between Dataflow and Dataproc operators, so the trap here is that candidates might confuse DataprocSubmitJobOperator (for Hadoop/Spark) with Dataflow operators, especially when the question mentions 'transform' without specifying the processing framework.
Detailed technical explanation
Under the hood, DataflowCreatePythonJobOperator uses the Dataflow API to stage the Python file and dependencies to Cloud Storage, then launches a Dataflow job with specified pipeline options (e.g., region, staging location). A subtle behavior is that it requires the Dataflow Runner v2 to be enabled for certain features like streaming engine, and the operator can block until job completion or run asynchronously depending on the `wait_until_finished` parameter. In real-world scenarios, this operator is essential for orchestrating complex ETL pipelines where Dataflow performs stateful transformations like windowing or side inputs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: DataflowCreatePythonJobOperator — B is correct because the DataflowCreatePythonJobOperator is specifically designed to submit and manage Apache Beam pipelines written in Python as Dataflow jobs in Google Cloud. This operator handles the creation of a Dataflow job from a Python file, which aligns with the requirement to run Dataflow transformations within a Cloud Composer DAG.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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