The answer is DataflowRunner, as it is the fully managed, serverless Apache Beam runner on Google Cloud specifically designed for batch pipelines that require reliability and scalability. This runner is the correct choice because it automatically handles resource provisioning, parallel processing, and exactly-once semantics, which are critical when reading Avro files from Cloud Storage and writing to BigQuery during daily runs and backfills. On the Google Professional Data Engineer exam, this scenario tests your understanding of which runner integrates natively with Google Cloud services and manages stateful batch processing without manual infrastructure management. A common trap is choosing SparkRunner or FlinkRunner, but those require self-managed clusters and lack the seamless BigQuery sink optimization of DataflowRunner. Remember the memory tip: for batch on Google Cloud, DataflowRunner is the "one-stop shop" — it handles both daily runs and backfills with automatic scaling and exactly-once processing, making it the default choice for any Apache Beam batch pipeline tied to Cloud Storage and BigQuery.
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 data engineer is designing a batch data pipeline that reads Avro files from Cloud Storage, transforms data using Apache Beam, and writes to BigQuery. The pipeline must handle daily runs and backfills. Which runner should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
DataflowRunner
DataflowRunner is the correct choice because it is the fully managed service runner for Apache Beam on Google Cloud, optimized for batch and streaming pipelines. It automatically handles scaling, resource management, and exactly-once processing semantics, which are essential for reliable daily runs and backfills with Avro files from Cloud Storage and BigQuery sinks.
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.
✗
FlinkRunner
Why it's wrong here
FlinkRunner requires a Flink cluster and adds operational complexity.
✓
DataflowRunner
Why this is correct
DataflowRunner is a fully managed service that supports batch pipelines, backfills, and direct integration with GCS and BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
✗
SparkRunner
Why it's wrong here
SparkRunner requires a Spark cluster (e.g., Dataproc) and does not provide managed backfill capabilities.
✗
DirectRunner
Why it's wrong here
DirectRunner is suitable for local testing, not production batch pipelines.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the runner with the execution engine, assuming that any distributed runner (Flink, Spark) is suitable for production, when the question specifically tests knowledge of Google Cloud-native services and the need for managed infrastructure for batch pipelines with backfills.
Detailed technical explanation
How to think about this question
DataflowRunner leverages the Google Cloud Dataflow service, which uses the Fn API for portable execution and provides autoscaling, dynamic work rebalancing, and exactly-once processing via checkpointing and commit logs. For backfills, Dataflow supports time-based windowing and triggers that can reprocess historical data without manual intervention, and it integrates directly with Cloud Storage via the AvroIO connector and BigQuery via the BigQueryIO connector, handling schema evolution and partitioning automatically.
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: DataflowRunner — DataflowRunner is the correct choice because it is the fully managed service runner for Apache Beam on Google Cloud, optimized for batch and streaming pipelines. It automatically handles scaling, resource management, and exactly-once processing semantics, which are essential for reliable daily runs and backfills with Avro files from Cloud Storage and BigQuery sinks.
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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Question Discussion
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