PDE Ingesting and Processing the Data Practice Question
Which Dataflow feature automatically scales the number of workers based on the pipeline's current workload, and also selects the optimal machine type for each worker based on the pipeline's resource requirements?
⚠ Common exam trap
Google often tests the distinction between horizontal autoscaling (adding/removing workers) and vertical autoscaling (changing machine type), and the trap here is that candidates assume Dataflow Shuffle or Streaming Engine handle scaling, when in fact they only optimize specific pipeline phases (shuffle or state management) without affecting worker count or machine type.
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
✓
Dataflow Prime
Dataflow Prime is the correct answer because it is the only Dataflow feature that provides both automatic worker scaling (horizontal autoscaling) and intelligent machine type selection (vertical autoscaling). It dynamically adjusts the number of workers based on the pipeline's current workload and selects the optimal machine type (e.g., CPU, memory, or accelerator-optimized) for each worker based on the pipeline's resource requirements, such as CPU utilization, memory pressure, or shuffle throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataflow Shuffle
Why it's wrong here
Shuffle is a service for efficient data shuffling, not autoscaling.
- ✓
Dataflow Prime
Why this is correct
Dataflow Prime provides vertical autoscaling, dynamically resizing worker machine types to match each stage's resource demands, alongside horizontal worker-count scaling. This satisfies the stem's dual requirement: automatic worker scaling plus optimal machine-type selection based on the pipeline's resource requirements.
- ✗
Dataflow Streaming Engine
Why it's wrong here
Streaming Engine moves state management from workers to backend, but does not right-fit machine types.
- ✗
Dataflow Flex Templates
Why it's wrong here
Flex Templates provide runtime parameterization, not vertical autoscaling.
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