The answer is to use DataprocJobOperator with a job_poll_interval and add a sensor to verify output. This is correct because the core issue is a race condition between Dataproc reporting job completion in Airflow metadata and the actual data being fully written to Cloud Storage; the job_poll_interval forces Airflow to poll the Dataproc API for true job status rather than relying on a premature success signal, while the sensor (e.g., checking for a marker file or row count in BigQuery) provides a final consistency check that the data is available and durable. On the Google Professional Data Engineer exam, this scenario tests your understanding of Cloud Composer DAG dependency data availability and the subtlety that asynchronous job operators can report success before side effects are committed—a common trap is assuming that a job’s API status equals data readiness. Remember the two-step rule: poll the job, then sense the data.
PDE Designing data processing systems Practice Question
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.
In Cloud Composer, a DAG has two tasks: task_A (runs an Apache Spark job on Dataproc) and task_B (loads data from Cloud Storage to BigQuery). task_B must start after task_A completes. The DAG is scheduled to run hourly. Sometimes task_B starts before task_A finishes because task_A's Dataproc job appears to complete in the Airflow metadata but the data is not yet available. What is the best way to ensure task_B only runs after the data is fully written?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: Retries happen after failure, not prevent premature start.
B
Use a sensor after task_A that checks for a specific file in Cloud Storage
Why wrong: This is a good approach but not the best because the sensor runs before task_A is considered done; it's better to use DataprocJobOperator with a poke context.
C
Use DataprocJobOperator with a job_poll_interval and add a sensor to verify output
DataprocJobOperator can poll the job status, and adding a sensor ensures data is written before proceeding.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use DataprocJobOperator with a job_poll_interval and add a sensor to verify output
Option C is correct because it addresses the root cause: the Dataproc job may report completion in Airflow metadata before the output data is fully written to Cloud Storage. By using DataprocJobOperator with a job_poll_interval, you ensure Airflow waits for the actual job completion on Dataproc, and adding a sensor to verify the output (e.g., checking for a success marker file or expected data in Cloud Storage) guarantees that task_B only starts after the data is fully available. This two-step approach prevents race conditions between job completion and data consistency.
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.
✗
Increase the number of retries for task_B
Why it's wrong here
Retries happen after failure, not prevent premature start.
✗
Use a sensor after task_A that checks for a specific file in Cloud Storage
Why it's wrong here
This is a good approach but not the best because the sensor runs before task_A is considered done; it's better to use DataprocJobOperator with a poke context.
✓
Use DataprocJobOperator with a job_poll_interval and add a sensor to verify output
Why this is correct
DataprocJobOperator can poll the job status, and adding a sensor ensures data is written before proceeding.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the DAG schedule to run every 30 minutes
Why it's wrong here
Does not fix the dependency issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume a job's completion status in Airflow metadata is sufficient to guarantee data availability, overlooking the eventual consistency of Cloud Storage and the fact that Dataproc job completion and data write finalization are not atomic.
Detailed technical explanation
How to think about this question
Under the hood, DataprocJobOperator uses the Dataproc API to submit a job and then polls its status at intervals defined by job_poll_interval (default 10 seconds). However, the job's 'DONE' status in the API may not guarantee that all output data has been flushed to Cloud Storage due to eventual consistency or buffered writes. Adding a sensor (e.g., GoogleCloudStorageObjectSensor) that waits for a specific marker file or checks for expected data size ensures that the data is fully written before proceeding, a pattern known as 'data availability gating' in production pipelines.
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 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.
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.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use DataprocJobOperator with a job_poll_interval and add a sensor to verify output — Option C is correct because it addresses the root cause: the Dataproc job may report completion in Airflow metadata before the output data is fully written to Cloud Storage. By using DataprocJobOperator with a job_poll_interval, you ensure Airflow waits for the actual job completion on Dataproc, and adding a sensor to verify the output (e.g., checking for a success marker file or expected data in Cloud Storage) guarantees that task_B only starts after the data is fully available. This two-step approach prevents race conditions between job completion and data consistency.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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