easyMultiple Choice
PDE Practice Question: A company processes CSV files that are uploaded…
A company processes CSV files that are uploaded to Cloud Storage by external partners. Each file is around 500 MB, and they need to be parsed and loaded into BigQuery. The processing must start as soon as the file arrives. What is the most efficient serverless architecture?
⚠ Common exam trap
Google Cloud often tests the misconception that Cloud Functions can handle large file processing directly, but the 9-minute timeout and memory limits make them unsuitable for files over a few hundred MB, pushing candidates toward the seemingly simpler Option D.
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 Storage triggers a Cloud Function that publishes events to Pub/Sub; a Dataflow streaming pipeline reads from Pub/Sub and writes to BigQuery.
It combines Cloud Storage event-driven triggers with Pub/Sub for reliable asynchronous message delivery, and uses Dataflow streaming with autoscaling to handle 500 MB files efficiently. This serverless architecture ensures processing starts immediately upon file arrival, scales to handle large files without manual intervention, and leverages BigQuery's streaming inserts for near-real-time data loading.
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 Storage triggers a Cloud Function that publishes events to Pub/Sub; a Dataflow streaming pipeline reads from Pub/Sub and writes to BigQuery.
Why this is correct
Cloud Storage object-finalise events trigger the Cloud Function, which publishes to Pub/Sub so Dataflow's streaming pipeline begins parsing immediately on arrival. This serverless chain handles 500 MB files without provisioning servers and satisfies the start-on-arrival requirement.
- ✗
Use Cloud Scheduler to periodically check for new files and process them with Dataflow batch jobs.
Why it's wrong here
Cloud Scheduler polling introduces latency between upload and processing, violating the start-on-arrival requirement, and Dataflow batch jobs are not event-driven. It is tempting because scheduled Dataflow pipelines are a familiar batch pattern, but that suits periodic aggregation, not immediate per-file ingestion.
- ✗
Cloud Storage triggers a Dataproc job that reads the file and loads it into BigQuery.
Why it's wrong here
Dataproc is a managed Spark and Hadoop service, not serverless; clusters take minutes to provision, so processing cannot start immediately on arrival. It is tempting because Dataproc handles large-scale parsing well, but that fits sustained batch workloads, not a single 500 MB file needing instant serverless execution.
- ✗
Cloud Storage triggers a Cloud Function that directly loads the data into BigQuery using the BigQuery API.
Why it's wrong here
A Cloud Function has limited memory and runtime, and loading 500 MB through the BigQuery API in-process is slow and error-prone; the load job itself is the correct serverless mechanism. It is tempting because event-driven functions feel lightweight, but they suit small transformations, not bulk ingestion.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.