Google ACE Planning and Configuring a Cloud Solution Practice Question
A developer needs to run a one-time SQL query against a large dataset stored in Cloud Storage in Parquet format. The query result will be used for ad-hoc analysis. They want to minimize cost and avoid provisioning any servers. Which service should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between serverless query services (BigQuery) and managed compute services (Dataproc, Compute Engine), where candidates mistakenly choose Dataproc thinking it is 'serverless' because it can be ephemeral, but it still requires provisioning VMs.
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
✓
BigQuery external table query
BigQuery external table query allows you to query data stored in Cloud Storage (including Parquet) directly without loading it, using BigQuery's serverless infrastructure. This minimizes cost because you only pay for the data scanned by the query, and you avoid provisioning any servers. It is ideal for one-time ad-hoc analysis of large datasets.
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 SQL import
Why it's wrong here
Cloud SQL cannot query Parquet files in Cloud Storage directly; import loads data into a managed instance, requiring provisioning and storage costs. It tempts because Cloud SQL runs SQL, and would be correct for persistent relational workloads, not ad-hoc queries over object storage.
- ✗
Dataproc cluster
Why it's wrong here
Dataproc provisions a cluster of VMs, contradicting the no-server requirement and incurring cluster costs for a one-off query. It tempts because Dataproc runs Spark and Hive over Cloud Storage data, and would be correct for recurring large-scale processing jobs rather than a single ad-hoc query.
- ✓
BigQuery external table query
Why this is correct
BigQuery external tables query Parquet data directly in Cloud Storage without loading or provisioning servers, so you pay only for the query bytes processed. This satisfies the one-time, ad-hoc, serverless and cost-minimising constraints, unlike loading into native tables or running Dataproc clusters.
- ✗
Compute Engine with Apache Spark installed
Why it's wrong here
Compute Engine VMs require provisioning and management, and Spark is unnecessary for a single SQL query over Parquet. It tempts because Spark handles large distributed datasets well, and would be correct for repeated, complex transformations rather than one ad-hoc query.
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 |
Go deeper
Related to this question
Learn chapter
Cloud Storage Classes and Lifecycle Rules
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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.