Cloud Digital Leader Google Cloud Products and Services Practice Question
A data analytics team wants to analyze large datasets using SQL and create dashboards with minimal latency. They need a serverless data warehouse and a BI tool. Which two services should they use? (Choose exactly 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
✓
Looker
BigQuery is a serverless data warehouse for SQL analytics. Looker is a BI platform integrated with BigQuery for dashboards. Dataflow is for data processing, not storage. Cloud Storage is for object storage, not SQL analytics. Looker Studio is free but less feature-rich for enterprise needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataflow
Why it's wrong here
Dataflow is a unified stream and batch data processing service, typically used for ETL, data pipeline construction, and real-time stream processing, rather than interactive analytics queries. It requires writing pipelines in Java or Python and is not designed as a query engine or BI tool. For analyzing large datasets with SQL or building dashboards, Dataflow is the wrong choice.
- ✗
Looker Studio
Why it's wrong here
Looker Studio is a free, lightweight data visualization and reporting tool that connects to various data sources, but it lacks the enterprise governance, versioning, and semantic modeling capabilities of Looker. It is primarily for simple dashboards and reports, not for large-scale, complex analytics or embedded BI. It cannot handle massive datasets natively and relies on the underlying database engine for query processing.
- ✗
Cloud Storage
Why it's wrong here
Cloud Storage is an object storage service for unstructured data like files, images, and backups, not a query engine or data warehouse. It does not support SQL or interactive analytics; you would need to load data from it into a data warehouse like BigQuery before analyzing. While it is a common component of a data lake architecture, it is not the right tool for directly analyzing large datasets.
- ✓
Looker
Why this is correct
Looker is an enterprise business intelligence and data analytics platform that provides a semantic modeling layer (LookML) to define business logic, enabling consistent and reusable metrics across the organization. It allows analysts to explore large datasets through a governed interface and create interactive dashboards, and it ties into cloud data warehouses like BigQuery for query execution. Looker is designed specifically for large-scale business analytics and is a correct choice for this use case.
- ✓
BigQuery
Why this is correct
BigQuery is a fully managed, serverless data warehouse that supports SQL queries on petabyte-scale datasets. It separates compute from storage, automatically scales resources, and offers features like partitioning, clustering, and BI Engine for fast interactive analysis. BigQuery is an appropriate tool for analyzing large datasets directly with standard SQL, making it a correct option for analytics teams.
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 Digital Transformation
Key term
Looker Studio
Looker Studio is a cloud-based data visualization and business intelligence platform that lets you create interactive dashboards and reports from various data sources.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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.