hardMultiple ChoiceObjective-mapped
PDE Practice Question: A healthcare company streams patient monitoring…
A healthcare company streams patient monitoring data to Cloud Pub/Sub. A Dataflow pipeline reads the stream, enriches with patient records from BigQuery, and writes to Bigtable for real-time queries. The BigQuery lookup is slow and causes pipeline lag. What is the best approach to improve performance?
⚠ Common exam trap
Many exam-takers assume that increasing parallelism (Option A) or using a faster read API (Option B) will solve the latency issue, when in fact the core problem is the synchronous per-element lookup pattern, which is best addressed by caching the reference data as a side input.
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 a side input from a BigQuery query with a global window and periodic refresh.
Using a side input from BigQuery with a global window and periodic refresh allows the Dataflow pipeline to cache the patient records in memory across all workers, avoiding per-element slow lookups. This pattern leverages Beam's side input semantics to broadcast a relatively static lookup table, significantly reducing latency compared to synchronous BigQuery queries for each incoming event.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of Dataflow workers and use vertical scaling.
Why it's wrong here
Does not fix the slow lookup.
- ✗
Use BigQuery's streaming read API in the pipeline.
Why it's wrong here
Streaming reads are still per-element.
- ✗
Pre-join the data in a batch pipeline and load into Bigtable.
Why it's wrong here
Batch adds latency, not real-time.
- ✓
Use a side input from a BigQuery query with a global window and periodic refresh.
Why this is correct
Side inputs cache data efficiently.
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