PDE Ingesting and Processing the Data Practice Question
A logistics company stores delivery manifests as newline-delimited JSON files in a Cloud Storage bucket. Analysts want to run SQL queries over these files immediately without importing them into a BigQuery dataset or paying for duplicate storage. Which BigQuery capability should they use?
⚠ Common exam trap
The trap here is reaching for a loading or streaming mechanism when the requirement explicitly forbids duplicating storage and demands immediate access.
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
✓
Create an external table over the Cloud Storage bucket using the JSON type.
External tables let BigQuery read newline-delimited JSON directly from Cloud Storage at query time, so analysts get immediate SQL access without loading data into managed storage. The files remain in the bucket, avoiding duplicate storage charges while still supporting standard SQL over the manifests.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the BigQuery Storage Write API to stream the files into a dataset.
Why it's wrong here
The Storage Write API is designed for high-throughput streaming ingestion into BigQuery tables, not for querying files in Cloud Storage. Using it would move the manifests into managed storage, duplicating the data and adding an ingestion step, which contradicts the stated goal of querying in place.
- ✗
Mount the bucket as a BigQuery dataset using the Cloud Storage connector.
Why it's wrong here
There is no feature that mounts a Cloud Storage bucket as a native BigQuery dataset. External tables are the supported mechanism for querying objects in place, and no connector turns a bucket into managed dataset storage, so this approach does not exist and would not meet the requirement.
- ✓
Create an external table over the Cloud Storage bucket using the JSON type.
Why this is correct
BigQuery external tables can point directly at newline-delimited JSON objects in Cloud Storage, letting analysts run SQL without loading the data into managed storage. This satisfies the requirement to query immediately and avoid duplicate storage costs, since the data stays in the bucket and is read at query time.
- ✗
Load the JSON files with a scheduled query that runs every hour.
Why it's wrong here
A scheduled query can automate loading, but it still copies the data into BigQuery managed storage, which creates the duplicate storage the company wants to avoid. It also introduces latency between upload and availability, so it does not provide immediate querying over the files in place.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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