Data Transformation with Google Cloud
An analytics team needs to run fast SQL queries across petabytes of historical sales data without managing servers or clusters. Which Google Cloud service is designed for this?
- ACloud Storage
- BBigQueryCorrect
- CCloud SQL
- DCompute Engine
Why: BigQuery is a fully managed, serverless data warehouse built for fast SQL analytics over very large datasets, scaling to petabytes without infrastructure management. Cloud SQL is a managed relational database aimed at transactional (OLTP) workloads, not petabyte-scale analytics. Compute Engine is raw VMs, and Cloud Storage is object storage, not a query engine. BigQuery fits analytical querying at scale.
Innovating with Google Cloud AI
A developer wants to add image-label detection to an app without collecting training data or building a model. Which Google Cloud option is the fastest path?
- ABuild a data warehouse in BigQuery first
- BCall the pre-trained Cloud Vision APICorrect
- CUse Cloud Spanner to store and classify images
- DTrain a custom model from scratch on Compute Engine
Why: The Cloud Vision API is a pre-trained service that detects objects, labels, text, and more from images with a simple API call, requiring no training data or model building. Training a custom model from scratch is slower and needs data and expertise. Spanner and BigQuery are data services, not vision models. Pre-trained APIs are the quickest route for common tasks.