PDE Storing the Data Practice Question
A data engineer wants to create a data lake on Google Cloud for storing raw streaming data, then transform it into curated and processed zones for analytics. The data is in Avro format and will be queried by BigQuery. Which two services are MOST suitable as the primary storage and query interface?
⚠ Common exam trap
Common misconception: candidates often think a processing engine like Dataproc is required to query Avro data in a data lake, but BigQuery can natively query Avro files stored in Cloud Storage without the need for intermediate processing.
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
✓
Cloud Storage and BigQuery
Cloud Storage is the most suitable primary storage for a data lake because it provides scalable, durable, and cost-effective object storage for raw Avro data. BigQuery is the ideal query interface because it can directly query Avro files stored in Cloud Storage using external tables, and it supports serverless analytics without needing to manage infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cloud Storage and BigQuery
Why this is correct
Cloud Storage provides the durable object store for raw Avro files, satisfying the data lake requirement, while BigQuery queries external data directly via its native Avro support and federated querying. This pairing separates cheap storage from serverless analytics, matching the raw-to-curated zone transformation the stem demands.
- ✗
Cloud Storage and Dataproc
Why it's wrong here
Cloud Storage lacks native support for Avro schema evolution and direct BigQuery streaming ingestion without intermediate conversion, while Dataproc introduces unnecessary batch-oriented processing overhead for a continuous streaming pipeline. This combination is tempting because Dataproc excels at large-scale ETL jobs using Spark or Hadoop, and Cloud Storage is a durable landing zone for raw data; it would be correct if the engineer needed to perform complex transformations on historical batches before loading into BigQuery, rather than querying streaming Avro data directly.
- ✗
Cloud Storage and Cloud SQL
Why it's wrong here
Cloud SQL is a relational OLTP database, not a query interface for Avro files held in a data lake; BigQuery cannot query Cloud SQL tables as external Avro data. Cloud SQL would be correct for transactional application backends, but the requirement is analytical querying of Avro objects in Cloud Storage.
- ✗
Cloud Storage and Firestore
Why it's wrong here
Firestore is a NoSQL document database for application data, not an analytics engine, and cannot query Avro files stored in Cloud Storage. It would be correct for mobile or web app state, but the scenario needs BigQuery to query Avro objects directly from the data lake.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.