- A
DataflowPythonOperator
Why wrong: This operator runs Dataflow pipelines, not Dataproc.
- B
BigQueryOperator
Why wrong: This operator runs BigQuery jobs, not Dataproc Spark.
- C
DataprocClusterCreateOperator
Why wrong: This operator creates a cluster, not submit a job.
- D
DataprocSubmitJobOperator
This operator submits a job (e.g., Spark, PySpark) to an existing Dataproc cluster.
Question 141 of 1,000
PDE Maintaining and Automating Data Workloads Practice Question
This PDE practice question tests your understanding of maintaining and automating data workloads. 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.
You need to schedule a Dataproc Spark job to run at 2 AM every day, and upon completion, trigger a BigQuery load job. Which Cloud Composer operator should you use to run the Spark job?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
DataprocSubmitJobOperator
Option D is correct because the DataprocSubmitJobOperator is specifically designed to submit a job (e.g., a Spark job) to an existing Dataproc cluster. In this scenario, you need to run a Spark job on a scheduled basis, and Cloud Composer (Airflow) provides this operator to submit the job to Dataproc. After the Spark job completes, you can chain a BigQuery load operator to trigger the load, matching the requirement exactly.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DataflowPythonOperator
Why it's wrong here
This operator runs Dataflow pipelines, not Dataproc.
- ✗
BigQueryOperator
Why it's wrong here
This operator runs BigQuery jobs, not Dataproc Spark.
- ✗
DataprocClusterCreateOperator
Why it's wrong here
This operator creates a cluster, not submit a job.
- ✓
DataprocSubmitJobOperator
Why this is correct
This operator submits a job (e.g., Spark, PySpark) to an existing Dataproc cluster.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse operators that manage cluster lifecycle (like DataprocClusterCreateOperator) with operators that submit jobs, or they mistakenly think DataflowPythonOperator can run Spark jobs because both are data processing frameworks.
Detailed technical explanation
How to think about this question
Under the hood, DataprocSubmitJobOperator sends a jobs.submit request to the Dataproc API, which creates a YARN application on the cluster. A subtle behavior is that this operator can be configured to wait for job completion (by setting `async=False`) or to run asynchronously, which is critical when chaining downstream tasks like BigQuery loads. In real-world pipelines, you often use this operator with a sensor (e.g., DataprocJobSensor) to ensure the job finishes before triggering the next step, avoiding race conditions.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Maintaining and Automating Data Workloads — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Maintaining and Automating Data Workloads — This question tests Maintaining and Automating Data Workloads — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: DataprocSubmitJobOperator — Option D is correct because the DataprocSubmitJobOperator is specifically designed to submit a job (e.g., a Spark job) to an existing Dataproc cluster. In this scenario, you need to run a Spark job on a scheduled basis, and Cloud Composer (Airflow) provides this operator to submit the job to Dataproc. After the Spark job completes, you can chain a BigQuery load operator to trigger the load, matching the requirement exactly.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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