easyMultiple Choice
PDE Practice Question: A company uses Cloud Dataflow to process…
A company uses Cloud Dataflow to process streaming data. They notice that the pipeline's throughput is lower than expected and the system is experiencing high latency. What is the most likely cause?
⚠ Common exam trap
A common misconception is that adding more workers always improves performance, but the key insight here is that too few workers directly cause high latency and low throughput in a streaming pipeline. The trap is to overlook the importance of sufficient worker scaling.
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
✓
Too few workers
In Cloud Dataflow, streaming pipelines require sufficient worker resources to handle the incoming data rate and maintain low latency. When too few workers are provisioned, the pipeline cannot process data quickly enough, leading to increased backlog and higher latency. This is the most likely cause of reduced throughput and high latency in a streaming pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Using batch mode instead of streaming mode
Why it's wrong here
The stem states the pipeline is already streaming, so batch mode cannot be the cause; batch mode would also fail to process unbounded data at all. It tempts because batch mode genuinely produces lower throughput and higher latency when a workload actually requires streaming execution.
- ✗
Too many workers
Why it's wrong here
Additional workers increase parallel processing capacity; they do not throttle throughput or add latency. It tempts because over-provisioning feels wasteful, and excess workers would be the answer if the symptom were cost overruns or quota exhaustion rather than slow processing.
- ✓
Too few workers
Why this is correct
Insufficient worker instances directly limit parallel processing capacity, so the pipeline cannot keep pace with incoming streaming data, producing the observed throughput drop and elevated latency. Cloud Dataflow scales horizontally by distributing work across workers; with too few allocated, backlog accumulates and per-element processing time rises.
- ✗
Incorrect watermark setting
Why it's wrong here
Watermarks govern event-time completeness and window firing, not raw pipeline throughput; a misconfigured watermark delays window results but does not reduce processing capacity. It tempts because watermark errors do cause latency in windowed aggregations, which is a different symptom from the throughput shortfall described.
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