The answer is to combine the `none_failed_or_skipped` trigger rule with a PythonOperator that counts upstream successes. This works because `none_failed_or_skipped` fires the downstream task only when every upstream task has either succeeded or been skipped, but it does not enforce a minimum number of actual successes—so you must add a conditional check inside a PythonOperator (or BranchPythonOperator) to verify that at least two of the three sensor tasks truly succeeded before proceeding. On the Google Professional Data Engineer exam, this scenario tests your understanding of Airflow’s trigger rules and custom logic for quorum-based dependencies, a common pattern in production pipelines where you need to run a downstream task if at least N upstream tasks succeed, rather than requiring all to pass. A frequent trap is assuming `all_done` or `one_success` alone will work; they either ignore failure states or don’t enforce a threshold. Memory tip: think “none_failed_or_skipped plus a counter” to build your quorum—Airflow handles the gate, Python handles the count.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
A data team uses Cloud Composer to orchestrate Airflow DAGs. They need to ensure that a downstream task runs only if at least two out of three upstream sensor tasks succeed. Which TWO configurations should they combine?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set trigger_rule to 'none_failed_or_skipped' and use a condition.
Option A is correct because the 'none_failed_or_skipped' trigger rule triggers the downstream task when all upstream tasks have succeeded or been skipped. Combined with a condition (e.g., using a PythonOperator or BranchPythonOperator) that checks whether at least two of the three sensor tasks succeeded, this ensures the downstream task runs only when the required threshold is met. This approach leverages Airflow's built-in trigger rules and conditional logic to implement a quorum-based dependency.
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.
✓
Set trigger_rule to 'none_failed_or_skipped' and use a condition.
Why this is correct
Combined with a condition, this ensures at least two succeeded.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set trigger_rule to 'one_success'.
Why it's wrong here
Triggers if any one task succeeds, not two.
✗
Set trigger_rule to 'all_done'.
Why it's wrong here
All_done triggers when all tasks are done, regardless of success.
✗
Set trigger_rule to 'none_failed'.
Why it's wrong here
Triggers if no tasks failed, but does not require a minimum number of successes.
✓
Use a PythonOperator to check the number of successes.
Why this is correct
Allows custom logic to count successes.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that a single trigger rule like 'one_success' or 'none_failed' can directly enforce a quorum condition, when in fact you must combine a trigger rule with explicit conditional logic to count successes.
Detailed technical explanation
How to think about this question
Under the hood, Airflow's trigger rules are evaluated after all upstream tasks have completed (i.e., reached a terminal state). The 'none_failed_or_skipped' rule sets the downstream task's state to 'success' only if every upstream task is in 'success' or 'skipped' state. By combining this with a PythonOperator that counts successes from XComs pushed by the sensors, you can implement a custom quorum check. In real-world scenarios, this pattern is useful for data pipelines that require consensus from multiple data sources (e.g., three file sensors waiting for files from different systems) before proceeding.
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.
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set trigger_rule to 'none_failed_or_skipped' and use a condition. — Option A is correct because the 'none_failed_or_skipped' trigger rule triggers the downstream task when all upstream tasks have succeeded or been skipped. Combined with a condition (e.g., using a PythonOperator or BranchPythonOperator) that checks whether at least two of the three sensor tasks succeeded, this ensures the downstream task runs only when the required threshold is met. This approach leverages Airflow's built-in trigger rules and conditional logic to implement a quorum-based dependency.
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: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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