Quick Answer
The answer is to ingest data into Cloud Storage and create BigQuery external tables. This architecture is correct because BigQuery external tables implement schema-on-read by allowing you to define the schema at query time over raw data files stored in Cloud Storage, rather than requiring a predefined schema at load time. This enables ad-hoc SQL queries directly against the data lake without the overhead of loading data into a separate system, making it a serverless and scalable solution. On the Google Professional Data Engineer exam, this question tests your understanding of how BigQuery decouples storage from compute, and a common trap is confusing external tables with native tables or assuming you must first load data into BigQuery storage. Remember the key distinction: external tables read from Cloud Storage on the fly, while native tables store data within BigQuery. A helpful memory tip is "external equals on-demand schema," meaning the schema is applied only when you query, not when you store.
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 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?
Ingest to Cloud Spanner, query directly.
Why wrong: Spanner is transactional.
Ingest to Cloud SQL, then export to Cloud Storage for queries.
Why wrong: Cloud SQL not for data lakes.
Ingest to Cloud Storage, create BigQuery external tables.
Schema-on-read and SQL.
Ingest to Cloud Storage, load into Dataproc for queries.
Why wrong: Requires cluster startup.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Spanner is transactional.
Ingest to Cloud SQL, then export to Cloud Storage for queries.
Why it's wrong here
Cloud SQL not for data lakes.
Ingest to Cloud Storage, create BigQuery external tables.
Why this is correct
Schema-on-read and SQL.
Related concept
Read the scenario before looking for a memorised answer.
Ingest to Cloud Storage, load into Dataproc for queries.
Why it's wrong here
Requires cluster startup.
Common exam traps
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.
Detailed technical explanation
BigQuery external tables use a federated query engine that reads data directly from Cloud Storage (e.g., Parquet, Avro, CSV) and applies the schema at query time via the table definition. This avoids data ingestion costs and enables real-time analysis on fresh data, but performance can be slower than native BigQuery tables due to lack of columnar storage optimization and caching. A real-world scenario is ingesting streaming logs into Cloud Storage and querying them with BigQuery external tables for immediate ad-hoc analysis without ETL.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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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