Google ACE Planning and Configuring a Cloud Solution Practice Question
A company wants to analyze terabytes of structured data using SQL queries. The data is stored in CSV files in Cloud Storage. Which TWO Google Cloud services can be used together to query the data directly without loading it into a database? (Choose 2)
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 can query external data sources using external tables or federated queries, including Cloud Storage. Cloud Storage is the storage location for the CSV files. BigQuery can directly query files in Cloud Storage using an external table or a federated query.
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 correct because it is a serverless, fully-managed data warehouse that can run SQL queries over terabytes of structured data with high speed. It supports direct querying of external data in Cloud Storage through federated queries or external tables, avoiding the need to import data. BigQuery's columnar storage and massive parallelism make it the appropriate analytical engine for this scenario.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is a relational database service (MySQL, PostgreSQL, or SQL Server) that stores data in internal volumes and requires importing data via dump files or client tools; it cannot directly reference CSV files stored in Cloud Storage. Its capacity and performance are designed for OLTP workloads, not for analyzing terabytes of data with large scans. For this reason, Cloud SQL is not a viable option for direct analysis of external structured files.
- ✗
Dataproc
Why it's wrong here
Dataproc is a managed service for running Apache Spark, Hadoop, or Flink clusters, and while it can process CSV files using Spark SQL or Hive, it requires provisioning and managing a compute cluster before any query can run. This adds operational overhead and startup time, and it is not an interactive query service like BigQuery. For a company wanting to analyze terabytes of structured data efficiently, Dataproc is overly complex and hence incorrect.
- ✗
Dataflow
Why it's wrong here
Dataflow is a unified stream and batch processing engine based on Apache Beam, designed for building ETL/ELT pipelines and event processing, not for interactive SQL querying. To analyze terabytes of structured data with standard SQL, a query engine is needed; Dataflow would require writing custom pipeline code to read, transform, and output results, making it unsuitable for ad-hoc analysis. Thus, it is incorrect for this use case.
- ✓
Cloud Storage
Why this is correct
Cloud Storage is the correct answer because it is the durable, highly-available object storage service where the CSV files should reside. Storing terabytes of structured data in Cloud Storage provides a cost-effective, scalable repository, and BigQuery can directly query these files via external tables without first loading them. This makes Cloud Storage the foundational storage layer for the analysis workload.
Go deeper
Related to this question
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Google Cloud Platform Overview
Key term
Cloud storage
Cloud storage is a service that lets you save data on remote servers accessed over the internet instead of on your computer's hard drive.
Key term
Google Cloud
Google Cloud is a suite of cloud computing services offered by Google that provides infrastructure, platform, and software solutions over the internet.
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JA
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.