- A
Read the BigQuery table as a side input and refresh it periodically using a global window with a periodic trigger
This approach allows the side input to be updated without restarting the pipeline, and the trigger ensures periodic refresh.
- B
Use a Combine.PerKey to group by product ID and then filter
Why wrong: This is inefficient and does not reference an external list of valid IDs.
- C
Use a custom pipeline option to read the valid IDs at startup and cache them
Why wrong: Caching at startup does not reflect updates to the BigQuery table unless the pipeline is restarted.
- D
Use a ParDo with a side input that is a MapSideInput of valid IDs, and refresh it on each element
Why wrong: Refreshing side input on each element is inefficient and not supported natively in Dataflow.
Quick Answer
The correct approach is to read the BigQuery table as a side input and refresh it periodically using a global window with a periodic trigger. This pattern is ideal because it allows the Dataflow pipeline to filter events with side input from BigQuery periodically refreshed, keeping the list of valid product IDs current without reprocessing the entire streaming event stream. On the Google Professional Data Engineer exam, this question tests your understanding of how to handle dynamic reference data in streaming pipelines—a common scenario where static side inputs would become stale. A frequent trap is choosing to re-read the entire BigQuery table on every element, which causes severe latency and cost, or using a side input without a trigger, which loads the data only once. Remember the memory tip: "Periodic trigger, global window—refresh the list, don't rewind the stream." This ensures low-latency filtering while the reference data stays reasonably up to date.
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.
A Dataflow pipeline reads events from Pub/Sub and transforms them. Some events contain invalid product IDs that should be filtered out. The list of valid product IDs is stored in a frequently updated BigQuery table. What is the best approach to filter out invalid events?
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.
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
Read the BigQuery table as a side input and refresh it periodically using a global window with a periodic trigger
Option A is correct because reading the BigQuery table as a side input with a global window and periodic trigger allows the pipeline to refresh the list of valid product IDs at a configurable interval without reprocessing the entire stream. This pattern is idiomatic for Beam/Dataflow when the reference data changes frequently and must be kept reasonably current while maintaining low latency for streaming events.
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.
- ✓
Read the BigQuery table as a side input and refresh it periodically using a global window with a periodic trigger
Why this is correct
This approach allows the side input to be updated without restarting the pipeline, and the trigger ensures periodic refresh.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a Combine.PerKey to group by product ID and then filter
Why it's wrong here
This is inefficient and does not reference an external list of valid IDs.
- ✗
Use a custom pipeline option to read the valid IDs at startup and cache them
Why it's wrong here
Caching at startup does not reflect updates to the BigQuery table unless the pipeline is restarted.
- ✗
Use a ParDo with a side input that is a MapSideInput of valid IDs, and refresh it on each element
Why it's wrong here
Refreshing side input on each element is inefficient and not supported natively in Dataflow.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that side inputs are static or that per-element refresh is feasible, leading candidates to choose Option D, but in reality side inputs are materialized once per window/trigger and cannot be efficiently updated per element.
Detailed technical explanation
How to think about this question
Under the hood, Beam's side input with a global window and periodic trigger uses a combination of a global window (all elements in one window) and a processing-time trigger (e.g., AfterProcessingTime) to re-read the BigQuery table at a specified interval. The side input is materialized as a PCollectionView, and the ParDo that filters events accesses this view as a map. This approach balances freshness with performance, as the side input is only refreshed on trigger firings, not on every element.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
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: Read the BigQuery table as a side input and refresh it periodically using a global window with a periodic trigger — Option A is correct because reading the BigQuery table as a side input with a global window and periodic trigger allows the pipeline to refresh the list of valid product IDs at a configurable interval without reprocessing the entire stream. This pattern is idiomatic for Beam/Dataflow when the reference data changes frequently and must be kept reasonably current while maintaining low latency for streaming events.
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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Last reviewed: Jun 30, 2026
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