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 stores the Avro files in zones, and BigQuery queries them via external tables or loaded tables.
- ✗
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 not suitable for data lake storage; it's transactional OLTP.
- ✗
Cloud Storage and Firestore
Why it's wrong here
Firestore is not designed for analytics queries at data lake scale.
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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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.