hardMultiple ChoiceObjective-mapped
Google ACE Practice Question: A team has a streaming pipeline built with Apache…
A team has a streaming pipeline built with Apache Beam that reads from Cloud Pub/Sub and writes transformed data to BigQuery. Which GCP service executes this pipeline with managed autoscaling?
⚠ Common exam trap
Many candidates confuse Cloud Dataproc (which runs Spark) with Cloud Dataflow (which runs Beam), not realizing that Beam pipelines require Dataflow for managed autoscaling, while Dataproc requires manual cluster sizing or separate autoscaling policies.
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
✓
Cloud Dataflow
Cloud Dataflow is the correct service because it is a fully managed, autoscaling service specifically designed to execute Apache Beam pipelines. It handles the reading from Cloud Pub/Sub and writing to BigQuery, automatically scaling worker resources based on the pipeline's processing demands.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Composer
Why it's wrong here
Cloud Composer is a fully managed Apache Airflow environment used for workflow orchestration, scheduling, and monitoring of multi-step tasks. Although you can use Composer to trigger or coordinate Dataflow jobs, it does not execute Apache Beam pipelines directly; it only calls external services via operators. The execution of Beam pipelines happens in a runner, and Cloud Composer is not a Beam runner, making it incorrect for this scenario.
- ✓
Cloud Dataflow
Why this is correct
Cloud Dataflow is the correct choice because it is the fully managed, native execution engine for Apache Beam pipelines on Google Cloud. When you run a Beam pipeline with the Dataflow runner, the service automatically provisions and autoscales workers for both streaming and batch modes, providing unified semantics. It handles resource management, checkpointing, and exactly-once processing without requiring you to manage clusters.
- ✗
Cloud Dataproc
Why it's wrong here
Cloud Dataproc is a managed service for running Apache Hadoop and Apache Spark clusters, but it does not natively execute Apache Beam pipelines. While you can theoretically run a Beam pipeline using the Spark runner on a Dataproc cluster, this requires manually configuring the cluster, choosing a non-Dataflow runner, and managing Spark dependencies. The question asks for the service that directly executes Beam pipelines, which is Dataflow, not Dataproc.
- ✗
Cloud Data Fusion
Why it's wrong here
Cloud Data Fusion is a visual data integration (ETL/ELT) platform that lets you design pipelines graphically. It generates the underlying code and then submits jobs to an execution engine such as Cloud Dataflow or Cloud Dataproc, but it does not itself execute Apache Beam pipelines. Data Fusion is a pipeline authoring and orchestration layer, not a Beam runner, so selecting it would miss the core execution capability required by the question.
Go deeper
Related to this question
Learn chapter
Google Cloud Platform Overview
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.
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.
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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.