PDE Storing the Data Practice Question
A company stores JSON-formatted application logs in Cloud Storage. They need to query these logs with SQL, but they want to avoid the cost and latency of loading them into BigQuery. The logs have a consistent schema, and queries will filter on a timestamp field and a few nested fields. Which approach should they use?
⚠ Common exam trap
The trap here is thinking that federated queries can access Cloud Storage files, when they are actually for external databases like Cloud SQL.
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 in BigQuery over the Cloud Storage JSON files and enable hive partitioning.
BigQuery external tables allow querying JSON files in Cloud Storage without loading, and hive partitioning enables partition pruning on the timestamp field, reducing scanned data and cost. This meets the requirement to avoid load costs and latency while supporting SQL queries. Loading into a native table, federated queries, or Dataproc all introduce extra cost or complexity and do not match the serverless, direct-query need.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create an external table in BigQuery over the Cloud Storage JSON files and enable hive partitioning.
Why this is correct
BigQuery external tables can query JSON files directly in Cloud Storage without loading. Enabling hive partitioning on a timestamp-derived folder structure allows partition pruning, reducing data scanned and improving performance. This matches the need to query with SQL while avoiding load costs and leverages the consistent schema and timestamp filter.
- ✗
Create a Dataproc cluster and run Spark SQL queries against the JSON files in Cloud Storage.
Why it's wrong here
Dataproc with Spark SQL can query JSON files, but it requires managing a cluster, which adds operational overhead and cost. It does not provide the serverless, pay-per-query model of BigQuery external tables. For ad-hoc SQL queries on logs, this is less efficient and more complex than using BigQuery directly.
- ✗
Load the JSON files into a BigQuery native table using schema autodetect and then delete the files.
Why it's wrong here
Loading into a native table provides fast queries, but it incurs storage and load costs, which the company explicitly wants to avoid. It also requires managing the load process and duplicates data. This approach contradicts the requirement to avoid loading and is not the best fit for querying logs directly from Cloud Storage.
- ✗
Use BigQuery federated queries with a Cloud SQL connection to query the JSON files.
Why it's wrong here
Federated queries connect to external databases like Cloud SQL or Spanner, not directly to Cloud Storage files. They cannot read JSON files from a bucket. This option misuses the feature and would not work for the described scenario. It also adds unnecessary complexity and does not avoid data movement.
Quick reference
Cloud Service Model Comparison
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|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
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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.