PDE Ingesting and Processing the Data Practice Question
A startup uploads JSON log files to a Cloud Storage bucket throughout the day and wants BigQuery to query them within minutes with no ETL code and no data duplication. The files use newline-delimited JSON and the schema is stable. The team wants the lowest-effort option that still lets queries see newly arrived files automatically. Which approach should they use?
⚠ Common exam trap
The trap here is assuming data must be loaded into BigQuery storage before it can be queried, when external tables query Cloud Storage in place.
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 a BigQuery external table over the Cloud Storage bucket using the newline-delimited JSON format
A BigQuery external table reads newline-delimited JSON directly from Cloud Storage, so no data is copied and no pipeline code is needed. Because the table definition is a URI pattern over the bucket, files added later are picked up by subsequent queries, which satisfies the fast-visibility and low-effort requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule a nightly bq load job to append the JSON files into a native BigQuery table
Why it's wrong here
A nightly load job only refreshes once per day, so the team's requirement to query new logs within minutes would not be met, and the load also duplicates the data into BigQuery storage. It additionally requires maintaining a scheduler and handling already-loaded files, which is more effort than querying in place.
- ✗
Build a Dataflow streaming job that parses the JSON and writes rows to BigQuery
Why it's wrong here
A Dataflow job would require writing and operating pipeline code, which contradicts the no-ETL-code requirement, and it copies the data into BigQuery storage. For stable newline-delimited JSON with no transformation needs, the streaming pipeline adds cost and maintenance without delivering any benefit the external table does not already provide.
- ✗
Use the Storage Write API to stream each uploaded file's contents directly into a BigQuery table
Why it's wrong here
The Storage Write API is a low-level write interface that requires client code to open streams, assign offsets, and handle retries, so it is far from code-free. It also duplicates the data into BigQuery storage and does not automatically detect new files in the bucket, meaning the team would still need a trigger mechanism.
- ✓
Create a BigQuery external table over the Cloud Storage bucket using the newline-delimited JSON format
Why this is correct
An external table over Cloud Storage lets BigQuery query the JSON files in place without loading or duplicating data, and newly added files that match the URI pattern are visible to subsequent queries automatically. No ETL code or pipeline is required, so this is the lowest-effort option that still reflects new arrivals within minutes.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.