- A
Export the entire database to Cloud Storage as CSV files every hour and load them into BigQuery using a load job with WRITE_TRUNCATE.
Why wrong: Incorrect. Exporting the entire database hourly is not near real-time and does not capture frequent updates and deletes efficiently. WRITE_TRUNCATE replaces the entire table, losing historical data and missing changes between exports.
- B
Use a Dataflow pipeline with JDBCIO to read from Cloud SQL every minute and write changes to BigQuery using upserts.
Why wrong: Incorrect. Dataflow with JDBCIO reads from Cloud SQL periodically, but it does not capture deletes and updates efficiently. JDBCIO is not designed for change data capture and cannot guarantee exactly-once delivery for upserts in near real-time.
- C
Use Cloud Data Fusion with a Debezium streaming source to capture CDC from Cloud SQL and a BigQuery sink with exactly-once mode.
Correct. Cloud Data Fusion with Debezium streaming source captures change data capture (CDC) from Cloud SQL PostgreSQL, handling inserts, updates, and deletes in near real-time. The BigQuery sink with exactly-once mode ensures no duplicate records, meeting all requirements.
- D
Use Cloud SQL's change data capture feature to write changes to a Pub/Sub topic and use a Dataflow pipeline to stream into BigQuery.
Why wrong: Incorrect. Cloud SQL does not have a native CDC feature to write changes directly to Pub/Sub. This approach would require an external CDC tool like Debezium or Datastream, making the option as stated infeasible.
Cloud SQL to BigQuery CDC — Near Real-Time with Debezium and Data Fusion | Google Professional Data Engineer Explained
This PDE practice question tests your understanding of building and operationalizing 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 company wants to replicate a Cloud SQL (PostgreSQL) database to BigQuery in near real-time for analytics. The volume is about 10GB per day with frequent updates and deletes. They need to capture changes with low latency and ensure exactly-once delivery to BigQuery. Which approach should they use?
Quick Answer
The answer is Cloud Data Fusion with a Debezium streaming source and a BigQuery sink in exactly-once mode. This combination is correct because Debezium captures change data capture (CDC) from Cloud SQL PostgreSQL by reading the database’s write-ahead log, which captures every insert, update, and delete with low latency, while Cloud Data Fusion’s BigQuery sink with exactly-once mode guarantees no duplicate records even during reprocessing. On the Google Professional Data Engineer exam, this scenario tests your understanding of near real-time replication patterns for analytics, often contrasting CDC-based streaming with batch ELT or Pub/Sub approaches; a common trap is choosing a simple batch export, which cannot handle frequent updates and deletes. Remember the memory tip: “Debezium logs the changes, Data Fusion sinks exactly once” — if you see frequent updates and deletes plus exactly-once delivery, think Debezium plus Data Fusion’s exactly-once mode.
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
Use Cloud Data Fusion with a Debezium streaming source to capture CDC from Cloud SQL and a BigQuery sink with exactly-once mode.
Option C is correct because Cloud Data Fusion with a Debezium streaming source provides native change data capture (CDC) from PostgreSQL, capturing inserts, updates, and deletes with low latency. The BigQuery sink in exactly-once mode ensures no duplicate records, meeting the requirement for near real-time analytics with frequent updates and deletes.
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.
- ✗
Export the entire database to Cloud Storage as CSV files every hour and load them into BigQuery using a load job with WRITE_TRUNCATE.
Why it's wrong here
Incorrect. Exporting the entire database hourly is not near real-time and does not capture frequent updates and deletes efficiently. WRITE_TRUNCATE replaces the entire table, losing historical data and missing changes between exports.
- ✗
Use a Dataflow pipeline with JDBCIO to read from Cloud SQL every minute and write changes to BigQuery using upserts.
Why it's wrong here
Incorrect. Dataflow with JDBCIO reads from Cloud SQL periodically, but it does not capture deletes and updates efficiently. JDBCIO is not designed for change data capture and cannot guarantee exactly-once delivery for upserts in near real-time.
- ✓
Use Cloud Data Fusion with a Debezium streaming source to capture CDC from Cloud SQL and a BigQuery sink with exactly-once mode.
Why this is correct
Correct. Cloud Data Fusion with Debezium streaming source captures change data capture (CDC) from Cloud SQL PostgreSQL, handling inserts, updates, and deletes in near real-time. The BigQuery sink with exactly-once mode ensures no duplicate records, meeting all requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud SQL's change data capture feature to write changes to a Pub/Sub topic and use a Dataflow pipeline to stream into BigQuery.
Why it's wrong here
Incorrect. Cloud SQL does not have a native CDC feature to write changes directly to Pub/Sub. This approach would require an external CDC tool like Debezium or Datastream, making the option as stated infeasible.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that Cloud SQL has a native CDC feature to write to Pub/Sub, but in reality, it requires an external CDC tool like Debezium or Datastream to capture changes.
Detailed technical explanation
How to think about this question
Debezium uses PostgreSQL's logical replication slots and the pgoutput plugin to stream row-level changes as events, capturing all DML operations including deletes. Cloud Data Fusion's Debezium source translates these events into structured records, and the BigQuery sink uses streaming inserts with deduplication keys to achieve exactly-once delivery, handling the 10GB/day volume efficiently. In real-world scenarios, this approach avoids the latency of batch exports and the data loss of snapshot-based polling.
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
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FAQ
Questions learners often ask
What does this PDE question test?
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: Use Cloud Data Fusion with a Debezium streaming source to capture CDC from Cloud SQL and a BigQuery sink with exactly-once mode. — Option C is correct because Cloud Data Fusion with a Debezium streaming source provides native change data capture (CDC) from PostgreSQL, capturing inserts, updates, and deletes with low latency. The BigQuery sink in exactly-once mode ensures no duplicate records, meeting the requirement for near real-time analytics with frequent updates and deletes.
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: Jun 30, 2026
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