PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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 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.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
BigQuery
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.
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.
✗
Cloud Filestore
Why it's wrong here
Filestore provides shared filesystems for VMs, not scalable object storage.
✓
BigQuery
Why this is correct
BigQuery can store and query structured data, and with federated queries it can access external files.
Related concept
Read the scenario before looking for a memorised answer.
✓
Cloud Storage
Why this is correct
Cloud Storage is object storage that can hold any file format and is highly scalable.
Related concept
Read the scenario before looking for a memorised answer.
✗
Cloud Spanner
Why it's wrong here
Spanner is a relational database with strong consistency, not for data lake storage.
✗
Cloud Bigtable
Why it's wrong here
Bigtable is a NoSQL wide-column database, not suitable for raw file storage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any database or storage service can serve as a data lake, but the trap here is that only object storage (Cloud Storage) and a serverless query engine (BigQuery) provide the schema-on-read flexibility and scalability required for raw multi-format data, while transactional or operational databases (Spanner, Bigtable) impose schema-on-write constraints and are not designed for bulk analytical storage.
Detailed technical explanation
How to think about this question
Cloud Storage uses a flat namespace with object versioning and lifecycle policies, enabling cost-effective tiering (e.g., Standard, Nearline, Coldline, Archive) for raw data. BigQuery's federated queries leverage the Storage API to read data directly from Cloud Storage, supporting schema auto-detection for CSV and JSON, while Parquet and Avro benefit from columnar pruning and predicate pushdown for efficient scanning. In real-world scenarios, combining Cloud Storage as the data lake with BigQuery as the query engine allows separation of storage and compute, enabling multiple processing frameworks (e.g., Spark for ETL, Dataflow for streaming) to access the same raw data without duplication.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BigQuery — 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.
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.
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Question Discussion
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