Quick Answer
The answer is to increase the number of Dataflow workers and set the --maxStreamingRowsToBundle parameter to a higher value. A deadline exceeded error in a streaming Dataflow pipeline occurs when a worker cannot complete its processing within the default timeout, often because the incoming Pub/Sub stream overwhelms the available compute resources. Increasing workers distributes the load across more virtual machines, reducing per-worker pressure, while raising maxStreamingRowsToBundle allows each worker to process a larger batch of rows per bundle, improving throughput and avoiding timeouts when writing to BigQuery. On the Google Professional Data Engineer exam, this scenario tests your understanding of autoscaling and bundle sizing in streaming pipelines—a common trap is to only scale workers without adjusting bundle parameters, which can still cause micro-batching bottlenecks. Remember the mnemonic "More workers, bigger bundles" to recall that both parallelism and batch size must be tuned to resolve streaming deadline errors.
This PDE practice question tests your understanding of designing data processing systems. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 pipeline uses Cloud Pub/Sub to ingest events, then a Cloud Dataflow job writes to BigQuery. The Dataflow job is failing with 'deadline exceeded' errors. Which TWO actions can resolve this? (Choose TWO.)
Increase the number of Dataflow workers.
Reduces load per worker.
Switch to BigQuery Storage Write API.
Why wrong: Addresses throughput, not timeout.
Decrease the batch size for writes to BigQuery.
Why wrong: May increase overhead.
Set the --maxStreamingRowsToBundle parameter to a higher value.
Increases timeout allowance.
Change the windowing from fixed to global.
Why wrong: May not address timeout.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the number of Dataflow workers.
Increasing the number of Dataflow workers (Option A) is correct because 'deadline exceeded' errors typically indicate that the pipeline is falling behind on processing due to insufficient parallelism. By adding more workers, the workload is distributed across more virtual machines, reducing the per-worker load and allowing the pipeline to keep up with the incoming Pub/Sub stream, thereby avoiding timeouts when writing to BigQuery.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the number of Dataflow workers.
Why this is correct
Reduces load per worker.
Related concept
Read the scenario before looking for a memorised answer.
Switch to BigQuery Storage Write API.
Why it's wrong here
Addresses throughput, not timeout.
Decrease the batch size for writes to BigQuery.
Why it's wrong here
May increase overhead.
Set the --maxStreamingRowsToBundle parameter to a higher value.
Why this is correct
Increases timeout allowance.
Related concept
Read the scenario before looking for a memorised answer.
Change the windowing from fixed to global.
Why it's wrong here
May not address timeout.
Common exam traps
Google Cloud often tests the misconception that 'deadline exceeded' errors are always caused by slow writes to the sink, leading candidates to choose options like decreasing batch size or switching write APIs, when the real issue is insufficient parallelism in the streaming pipeline.
Detailed technical explanation
Under the hood, Dataflow's autoscaling algorithm monitors CPU utilization and backlog size; 'deadline exceeded' errors often occur when the pipeline's streaming engine cannot commit output to BigQuery within the 10-minute default RPC deadline. Increasing workers triggers horizontal scaling, but note that if the bottleneck is at the BigQuery side (e.g., quota limits), adding workers may exacerbate the issue. A real-world scenario is a sudden spike in Pub/Sub traffic that outpaces the initial worker count, causing a growing backlog and eventual timeouts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the number of Dataflow workers. — Increasing the number of Dataflow workers (Option A) is correct because 'deadline exceeded' errors typically indicate that the pipeline is falling behind on processing due to insufficient parallelism. By adding more workers, the workload is distributed across more virtual machines, reducing the per-worker load and allowing the pipeline to keep up with the incoming Pub/Sub stream, thereby avoiding timeouts when writing to BigQuery.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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