hardMultiple ChoiceObjective-mapped
Google ACE Practice Question: A team is designing a data pipeline: Cloud…
A team is designing a data pipeline: Cloud Storage → Pub/Sub → Dataflow → BigQuery. They expect 50 GB of data per hour. Dataflow jobs must automatically scale workers based on pipeline backlog. Which Dataflow feature provides automatic horizontal scaling of worker VMs?
⚠ Common exam trap
Google Cloud often tests the distinction between horizontal and vertical scaling, and candidates may confuse Dataflow's autoscaling with GKE cluster autoscaler, not realizing Dataflow manages its own worker fleet independently of GKE.
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 Horizontal Autoscaling — automatically adds/removes workers based on pipeline lag
Dataflow Horizontal Autoscaling is the correct feature because it automatically adds or removes worker VMs based on the pipeline's backlog (lag), which directly matches the requirement for automatic horizontal scaling. This feature uses the Cloud Monitoring service to track the number of unprocessed elements and adjusts worker count accordingly, ensuring efficient resource usage without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertical scaling — Dataflow automatically increases worker machine types under load
Why it's wrong here
Dataflow scales horizontally (adds/removes workers) — it doesn't automatically change machine types (vertical scaling) during a job run.
- ✓
Dataflow Horizontal Autoscaling — automatically adds/removes workers based on pipeline lag
Why this is correct
Dataflow's horizontal autoscaler monitors pipeline backlog and adjusts the number of worker VMs to maintain throughput — enabled by default for streaming and configurable for batch.
- ✗
GKE cluster autoscaler — Dataflow runs on GKE and inherits its autoscaling
Why it's wrong here
Dataflow does not run on GKE — it manages its own worker VM fleet. The GKE autoscaler is irrelevant for Dataflow.
- ✗
Cloud Monitoring alerting policy that triggers worker additions via gcloud
Why it's wrong here
Manual alerting-based scaling adds latency and is error-prone — Dataflow's built-in autoscaler handles this automatically.
Go deeper
Related to this question
Learn chapter
Google Cloud Platform Overview
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
Key term
Pub/Sub
Pub/Sub is a messaging pattern where publishers send messages without knowing who receives them, and subscribers receive only the messages they care about.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the ACE exam.