- A
Bigtable for raw data and Cloud Spanner for processed data
Why wrong: Bigtable is a NoSQL database for transactional workloads, not ideal for binary files; Cloud Spanner is for globally distributed transactions, not analytics.
- B
Cloud Storage for both raw and processed data
Why wrong: While Cloud Storage can store processed data, it does not natively support SQL querying; this requires additional tools.
- C
Cloud SQL for raw data and Cloud Dataproc for processing
Why wrong: Cloud SQL has limited storage capacity and is not designed for raw binary data.
- D
Cloud Storage for raw data and BigQuery for processed data
Cloud Storage stores any file type cost-effectively, and BigQuery provides fast SQL queries on structured data.
Question 650 of 1,000
easyMultiple ChoiceObjective-mapped
Designing a Scalable Data Lake Using Cloud Storage and BigQuery
This PDE practice question tests your understanding of pde exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to implement a data lake on Google Cloud to store raw sensor data (unstructured binary files) and allow data scientists to run SQL queries on processed data. They expect to store terabytes of data and have different access patterns. Which combination of GCP services best meets these requirements?
Quick Answer
The correct combination is Cloud Storage for raw data and BigQuery for processed data. This works because Cloud Storage provides cost-effective, durable object storage ideal for unstructured binary sensor data at petabyte scale, with lifecycle management for different access patterns, while BigQuery offers a serverless, columnar SQL engine that automatically optimizes analytical queries on processed, structured data. On the Google Professional Data Engineer exam, this scenario tests your understanding of the separation between raw storage and analytics layers—a common trap is choosing a single service like BigQuery for everything, forgetting it cannot efficiently store raw binary blobs. Remember the memory tip: "Raw in the bucket, clean in the query"—Cloud Storage is the bucket for raw files, BigQuery is the query engine for processed tables.
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 for raw data and BigQuery for processed data
Cloud Storage is the ideal service for storing raw, unstructured binary sensor data at petabyte scale, offering low-cost, durable object storage with multiple access tiers. BigQuery is a serverless, highly scalable data warehouse that allows data scientists to run SQL queries on processed data, with features like columnar storage and automatic optimization for analytical workloads. This combination directly addresses the need for raw storage and SQL-based analytics on processed data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bigtable for raw data and Cloud Spanner for processed data
Why it's wrong here
Bigtable is a NoSQL database for transactional workloads, not ideal for binary files; Cloud Spanner is for globally distributed transactions, not analytics.
- ✗
Cloud Storage for both raw and processed data
Why it's wrong here
While Cloud Storage can store processed data, it does not natively support SQL querying; this requires additional tools.
- ✗
Cloud SQL for raw data and Cloud Dataproc for processing
Why it's wrong here
Cloud SQL has limited storage capacity and is not designed for raw binary data.
- ✓
Cloud Storage for raw data and BigQuery for processed data
Why this is correct
Cloud Storage stores any file type cost-effectively, and BigQuery provides fast SQL queries on structured data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that Cloud Storage can serve as a queryable database for SQL, when in fact it requires an external query engine like BigQuery or Dataproc for SQL access.
Detailed technical explanation
How to think about this question
Cloud Storage uses a flat namespace with object versioning and lifecycle policies to manage terabytes of sensor data cost-effectively, while BigQuery leverages a distributed, columnar storage format (Capacitor) and a Dremel execution engine to run SQL queries on processed data with sub-second latency on large datasets. A real-world scenario involves ingesting IoT sensor data as binary blobs into Cloud Storage, then using Cloud Dataflow or Dataproc to transform and load it into BigQuery for ad-hoc analysis, avoiding the need for traditional ETL pipelines.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Cloud Storage for raw data and BigQuery for processed data — Cloud Storage is the ideal service for storing raw, unstructured binary sensor data at petabyte scale, offering low-cost, durable object storage with multiple access tiers. BigQuery is a serverless, highly scalable data warehouse that allows data scientists to run SQL queries on processed data, with features like columnar storage and automatic optimization for analytical workloads. This combination directly addresses the need for raw storage and SQL-based analytics on processed data.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on PDE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is designing a data lake on Google Cloud. They need to store raw data in multiple formats (CSV, Parquet, Avro) and allow various downstream processing frameworks. Which two storage solutions provide flexibility and scalability? (Choose two.)
medium- A.Cloud Filestore
- ✓ B.BigQuery
- ✓ C.Cloud Storage
- D.Cloud Spanner
- E.Cloud Bigtable
Why B: BigQuery is correct because it can directly query raw data stored in Cloud Storage in formats like CSV, Parquet, and Avro using external tables or federated queries, without requiring data loading. This provides a flexible, serverless analytics layer that scales automatically and integrates with downstream processing frameworks like Apache Spark, Dataflow, and Dataproc.
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Last reviewed: Jun 30, 2026
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.
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