The answer is to use a Dataflow pipeline that reads deletion IDs from Pub/Sub, joins with the BigQuery transactions table using a side input, and writes the filtered data to a new table before swapping. This approach is correct because it avoids costly and slow DELETE mutations on large, date-partitioned tables, instead leveraging Dataflow’s scalable stream processing to handle high-throughput GDPR deletion requests efficiently within the 48-hour SLA. On the Google Professional Data Engineer exam, this scenario tests your understanding of cost-effective BigQuery data manipulation patterns and the trade-offs between DML statements and ETL-based rewrites. A common trap is choosing a solution that runs DELETE statements directly on the source table, which consumes significant slot resources and can fail under heavy load. Memory tip: think “filter and swap, don’t delete and weep”—rewriting the table with a side input is both scalable and budget-friendly for right-to-be-forgotten compliance.
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 financial services company must comply with GDPR "right to be forgotten". They store customer transactions in BigQuery partitioned by date. When a user requests deletion, all their data must be removed within 48 hours. The deletion requests are received via a Pub/Sub topic. What is the most scalable and cost-effective approach?
Use Cloud Functions to execute a BigQuery DELETE statement on each request
Why wrong: Each DELETE scans the entire table, which is costly and slow for large tables.
B
Use Cloud DLP to redact the user's data in Cloud Storage
Why wrong: Cloud DLP is for data loss prevention, not deletion from BigQuery.
C
Use a Dataflow pipeline that reads the deletion IDs from Pub/Sub, joins with the transactions table using a side input, and writes the filtered data to a new table, then swapping
This scales well and avoids full table scans; the side input contains the IDs to delete.
D
Use BigQuery table snapshots and restore after deletion
Why wrong: Snapshots are for point-in-time recovery, not selective deletion; restoring would undo other deletions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a Dataflow pipeline that reads the deletion IDs from Pub/Sub, joins with the transactions table using a side input, and writes the filtered data to a new table, then swapping
Option C is correct because it uses Dataflow to process deletion requests from Pub/Sub, join them with the BigQuery transactions table via a side input, and write a filtered copy to a new table. This approach is scalable (handles high-throughput streaming deletions) and cost-effective (avoids expensive DELETE mutations on BigQuery, which consume slot resources and can be slow for large tables). Swapping the new table for the old one completes the deletion efficiently within the 48-hour SLA.
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.
✗
Use Cloud Functions to execute a BigQuery DELETE statement on each request
Why it's wrong here
Each DELETE scans the entire table, which is costly and slow for large tables.
✗
Use Cloud DLP to redact the user's data in Cloud Storage
Why it's wrong here
Cloud DLP is for data loss prevention, not deletion from BigQuery.
✓
Use a Dataflow pipeline that reads the deletion IDs from Pub/Sub, joins with the transactions table using a side input, and writes the filtered data to a new table, then swapping
Why this is correct
This scales well and avoids full table scans; the side input contains the IDs to delete.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use BigQuery table snapshots and restore after deletion
Why it's wrong here
Snapshots are for point-in-time recovery, not selective deletion; restoring would undo other deletions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that BigQuery DELETE statements are the simplest way to remove data, but the trap here is that DELETE operations on large partitioned tables are expensive and not scalable for streaming deletion requests, whereas a Dataflow-based rewrite is both cost-effective and meets the 48-hour SLA.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow uses Apache Beam's side input pattern to join a streaming Pub/Sub PCollection of deletion IDs with a bounded PCollection from BigQuery (the transactions table). The pipeline applies a ParDo transform that filters out rows matching deletion IDs, then writes the result to a new BigQuery table using a batch load or streaming inserts. Swapping tables is atomic via a rename operation, ensuring consistency. In real-world scenarios, this pattern also handles late-arriving deletion requests by using a sliding window or global window with triggers, and it can be optimized with BigQuery's clustering to reduce shuffle costs.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 a Dataflow pipeline that reads the deletion IDs from Pub/Sub, joins with the transactions table using a side input, and writes the filtered data to a new table, then swapping — Option C is correct because it uses Dataflow to process deletion requests from Pub/Sub, join them with the BigQuery transactions table via a side input, and write a filtered copy to a new table. This approach is scalable (handles high-throughput streaming deletions) and cost-effective (avoids expensive DELETE mutations on BigQuery, which consume slot resources and can be slow for large tables). Swapping the new table for the old one completes the deletion efficiently within the 48-hour SLA.
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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