easyMultiple ChoiceObjective-mapped
PDE Practice Question: Process large CSV files stored in Cloud Storage…
A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated daily and each file is about 10 GB. The data is not time-sensitive and can be processed within a 24-hour window. Which service is most cost-effective for this use case?
⚠ Common exam trap
Many exam-takers choose Dataflow (Option B) because it is a popular batch processing service, but they overlook that Dataproc Serverless is more cost-effective for non-time-sensitive, large CSV batch jobs due to its serverless pricing model and native Spark support for CSV processing.
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
✓
Dataproc Serverless with PySpark
Dataproc Serverless with PySpark is the most cost-effective choice because it eliminates cluster management overhead and automatically scales resources based on workload, charging only for the processing time used. For 10 GB CSV files processed daily within a 24-hour window, the serverless model avoids the fixed costs of a persistent cluster, making it ideal for batch, non-time-sensitive jobs. PySpark's native support for CSV parsing and BigQuery integration via the Spark BigQuery connector ensures efficient data loading without additional services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Dataproc Serverless with PySpark
Why this is correct
Dataproc Serverless is cost-effective and suitable for batch processing of large CSVs.
- ✗
Dataflow with batch mode
Why it's wrong here
Dataflow is more expensive for batch than Dataproc Serverless.
- ✗
Cloud Data Fusion
Why it's wrong here
Data Fusion is a full ETL tool with higher costs and complexity.
- ✗
BigQuery Data Transfer Service
Why it's wrong here
Data Transfer Service is for scheduled transfers, not processing.
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.