PDE Storing the Data Practice Question
A company is designing a data lake on Cloud Storage with BigLake tables for unified governance. Which TWO statements about BigLake are correct? (Choose 2.)
⚠ Common exam trap
A common misconception is that BigLake requires data to be loaded into BigQuery storage, when in fact it is designed to query external data in Cloud Storage directly.
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
✓
BigLake tables allow querying data in Cloud Storage using BigQuery without loading into BigQuery storage.
Option B is correct because BigLake tables are external tables that let BigQuery query data residing in Cloud Storage (Parquet, ORC, Avro, JSON, CSV, Iceberg, Delta) directly, without ingesting it into BigQuery's native storage. Option D is correct because BigLake extends BigQuery's fine-grained governance to external data, supporting row-level security and column-level security (via policy tags in Data Catalog) so access controls apply uniformly across the lakehouse. Option A is wrong because BigLake's defining feature is avoiding the load into BigQuery storage; that describes native BigQuery tables. Option C is wrong because BigLake handles structured and semi-structured formats in Cloud Storage, not only structured data in BigQuery storage. Option E is wrong because BigLake does not perform automatic format conversion of CSV to Parquet; it queries files in their existing format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BigLake requires data to be loaded into BigQuery storage.
Why it's wrong here
BigLake tables can point at Cloud Storage objects directly, so loading into BigQuery storage is not required. It is tempting because native BigQuery tables do need loading, and BigLake governance resembles BigQuery's; the difference is the external table definition over Cloud Storage.
- ✓
BigLake tables allow querying data in Cloud Storage using BigQuery without loading into BigQuery storage.
Why this is correct
BigLake's external table capability lets BigQuery query Cloud Storage data in place, so no ingestion into BigQuery-managed storage is needed. This directly satisfies the stem's data lake design, where unified governance across open formats matters more than duplicating data, and it underpins the federated query pattern the scenario requires.
- ✗
BigLake only supports structured data in BigQuery storage.
Why it's wrong here
BigLake supports structured and unstructured data across Cloud Storage and other sources, not only structured data in BigQuery storage. It is tempting because BigQuery native tables are structured and stored internally, which is exactly the model BigLake was built to extend beyond.
- ✓
BigLake supports row-level and column-level security.
Why this is correct
BigLake enforces fine-grained access control directly on Cloud Storage data through row-level and column-level security policies, satisfying the unified governance constraint. This lets administrators restrict specific rows and columns for different users without duplicating data or creating separate views, unlike plain external tables that lack such native policy enforcement.
- ✗
BigLake automatically converts CSV files to Parquet.
Why it's wrong here
BigLake queries external data in place; it never rewrites CSV into Parquet, so no automatic format conversion occurs. The temptation is real because BigLake does improve query performance on external files, but that comes from metadata caching and open formats you supply yourself, not from transcoding.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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