PDE Storing the Data Practice Question
A retail company uploads daily sales CSV files to a Cloud Storage bucket. The analytics team wants BigQuery to automatically detect the schema and make new files queryable without running load jobs. The files are added with date-based prefixes, and the team wants to minimize operational overhead. Which BigQuery feature should they use?
⚠ Common exam trap
The trap here is reaching for a load job or pipeline when an external table with autodetect already reads new files in place with no ingestion operations.
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
✓
A BigQuery external table with schema autodetect over the Cloud Storage prefix.
An external table over the Cloud Storage prefix with schema autodetect lets BigQuery read the CSV files directly. New files that match the prefix appear in query results without any load jobs, which precisely fits the requirement to minimize operational overhead while keeping daily files queryable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Cloud Dataflow streaming pipeline that writes rows into BigQuery.
Why it's wrong here
Dataflow provides streaming ingestion but requires developing, deploying, and operating a pipeline. For daily CSV files that only need to be queryable, this is far more operational effort than necessary and does not leverage the existing file layout, so it is not the appropriate choice.
- ✗
A BigQuery native table loaded with a recurring scheduled query.
Why it's wrong here
A scheduled query can load data periodically, but it still requires managing the load job, schema, and schedule. It adds operational overhead and does not automatically expose new files as they arrive, so it does not satisfy the goal of minimizing overhead while making new files immediately queryable.
- ✗
A BigQuery table partitioned by ingestion time with a load job per file.
Why it's wrong here
Ingestion-time partitioning still requires a load job for each file and a defined schema, which conflicts with the autodetect and zero-load-job requirement. It also does not automatically pick up new files, so the team would need additional orchestration, increasing rather than minimizing operational overhead.
- ✓
A BigQuery external table with schema autodetect over the Cloud Storage prefix.
Why this is correct
BigQuery external tables can point at a Cloud Storage URI prefix, and with autodetect BigQuery infers column names and types from the files. New files matching the prefix become queryable without load jobs, which matches the low-overhead requirement and the date-prefixed upload pattern.
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Written and reviewed by Johnson Ajibi, MSc IT Security
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.