You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in…

Question

Options

• AIncrease the number of max workers
• BUse a larger instance type for your Cloud Dataflow workers
• CChange the zone of your Cloud Dataflow pipeline to run in us-central1
• DCreate a temporary table in Cloud Bigtable that will act as a buffer for new data.
• ECreate a temporary table in Cloud Spanner that will act as a buffer for new data.

• A
  84% (52)
• C
  2% (1)
• D
  5% (3)
• E
  10% (6)

Explanation

When all workers are at maximum CPU utilization, the two direct levers are scaling out (A: more workers) and scaling up (B: larger instance type) - both increase total compute capacity without changing the pipeline's architecture or data flow.

Why the distractors are wrong:

• C is incorrect because moving to us-central1 would move the pipeline outside the EU, potentially violating data residency requirements, and geographic location doesn't fix a CPU bottleneck.
• D and E are incorrect because adding a buffer in Bigtable or Spanner doesn't process the backlog faster - it just delays the problem. The bottleneck is compute, not ingestion ordering, and introducing an extra hop adds latency and cost without addressing CPU saturation.

Memory tip: Think of it as a factory assembly line that's overwhelmed - you either hire more workers (A) or give each worker better tools/a bigger workstation (B). Moving the factory to another city (C) or adding a waiting room (D/E) doesn't help the workers go faster.

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