hardMultiple Choice
PDE Practice Question: Building a data lake on Cloud Storage with data…
A company is building a data lake on Cloud Storage with data from multiple sources. They need to apply schema-on-read and support ad-hoc SQL queries. Which architecture is most suitable?
⚠ Common exam trap
Google Cloud often tests the distinction between schema-on-read (BigQuery external tables) and schema-on-write (traditional databases like Cloud Spanner or Cloud SQL), where candidates mistakenly choose a transactional database for analytical workloads.
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
✓
Ingest to Cloud Storage, create BigQuery external tables.
BigQuery external tables allow schema-on-read by defining the schema at query time over data stored in Cloud Storage, enabling ad-hoc SQL queries without loading data into a separate system. This architecture directly supports the requirement for schema-on-read and SQL-based analysis, as BigQuery provides a serverless, scalable SQL engine.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ingest to Cloud Spanner, query directly.
Why it's wrong here
Cloud Spanner is a globally distributed OLTP database requiring a fixed schema and does not perform schema-on-read over Cloud Storage objects. It is tempting for its SQL support, but that applies to transactional workloads, whereas the data lake needs external tables or a query engine reading files in situ.
- ✗
Ingest to Cloud SQL, then export to Cloud Storage for queries.
Why it's wrong here
Cloud SQL is a managed relational database requiring schema-on-write, so ingesting multi-source data there imposes a fixed schema before storage. It is tempting for familiar SQL querying, its actual purpose, but the stem needs schema-on-read against Cloud Storage, which BigQuery external tables deliver.
- ✓
Ingest to Cloud Storage, create BigQuery external tables.
Why this is correct
BigQuery external tables query Cloud Storage data directly, preserving the raw files without loading or transformation. This satisfies schema-on-read, since the schema is applied at query time rather than ingest, and supports ad-hoc SQL through BigQuery's engine. The multi-source constraint is met because each external table maps to its own source path.
- ✗
Ingest to Cloud Storage, load into Dataproc for queries.
Why it's wrong here
Loading Cloud Storage data into Dataproc means querying HDFS or cluster-local storage rather than the data lake directly, and it provisions clusters rather than offering serverless ad-hoc SQL. It is tempting for Spark-based batch processing, its real purpose, but BigQuery external tables or Dataproc Serverless query Cloud Storage without loading.
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 |
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