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Cloud Digital Leader Google Cloud Products and Services Practice Question

A company wants to analyze petabytes of sales data using SQL queries with sub-second response times for dashboards. They need a fully managed, serverless solution that separates storage and compute. Which service meets these requirements?

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

BigQuery is a serverless data warehouse that stores petabytes and uses SQL with fast query performance via columnar storage and separation of compute and storage.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • BigQuery

    Why this is correct

    BigQuery is a serverless, fully managed data warehouse with columnar storage and a distributed execution engine, purpose-built for petabyte-scale SQL analytics. It separates compute from storage, enabling sub-second interactive queries over massive datasets without provisioning clusters. Its native support for standard SQL, partitioning, and clustering makes it the ideal service for analyzing petabytes of sales data.

  • Cloud SQL

    Why it's wrong here

    Cloud SQL is a managed relational database service for online transaction processing (OLTP), offering MySQL, PostgreSQL, and SQL Server instances. It is typically limited to a few terabytes of storage and lacks the distributed, columnar architecture required to scan petabytes efficiently. Its query engine is optimized for low-latency single-row lookups and small transactions, not for high-throughput analytical aggregations across massive datasets.

  • Cloud Spanner

    Why it's wrong here

    Cloud Spanner provides globally distributed, strongly ACID-compliant transactional consistency combined with horizontal scaling, but it is fundamentally an OLTP database. Its row-oriented storage and query planner are optimized for point reads, index lookups, and write-heavy workloads, not for massive parallel scans or complex analytical SQL queries. Using Spanner for petabyte-scale sales analytics would result in poor performance and unnecessarily high costs compared to BigQuery.

  • Dataflow

    Why it's wrong here

    Dataflow is a fully managed stream and batch processing service based on Apache Beam, designed for building data pipelines such as ETL, streaming, and event processing. It is not a query engine and does not offer ad-hoc SQL analytics; even its SQL-like interface (Beam SQL) is intended for pipeline transformations, not interactive exploration. Dataflow can be used to clean and load sales data into BigQuery, but it cannot replace BigQuery as the serving layer for analytical SQL queries.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This GCDL 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 GCDL exam.