PDE Designing Data Processing Systems Practice Question
A company is evaluating BigQuery for a data warehouse migration. They have a mix of reporting queries and ad-hoc analytical queries. They want to control query costs and prevent runaway queries. Which THREE strategies should they implement?
⚠ Common exam trap
PDE often tests the difference between cost-control mechanisms (quotas, partitioning, reservations) and access/performance mechanisms (authorized views, materialized views), causing candidates to pick visibility or caching features as cost controls.
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
✓
Set a custom quota for concurrent queries
Option B is correct because setting a custom quota for concurrent queries (via BigQuery custom quotas in Cloud Console/IAM) caps the number of simultaneously running queries per project or user, preventing a flood of ad-hoc queries from exhausting slots and causing runaway costs. Option C is correct because partitioning (e.g., by ingestion/date column) and clustering (e.g., by frequently filtered columns) prune the data scanned, directly reducing bytes processed and therefore on-demand query cost. Option E is correct because BigQuery reservations with flex slots (or committed slots) let predictable reporting workloads run on dedicated capacity with fixed pricing, isolating them from unpredictable ad-hoc on-demand costs and giving cost predictability. Option A is not correct because authorized views control data access/visibility, not query cost or runaway query prevention. Option D is not correct because materialized views can accelerate specific recurring queries but do not by themselves control costs or prevent runaway queries across a mixed workload.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Grant authorized view access to limit data visibility
Why it's wrong here
Authorized views grant a view access to underlying datasets, controlling data visibility rather than bytes scanned or query spend. They suit sharing restricted subsets across teams; cost governance instead needs custom quotas, maximum bytes billed, or reservation and partitioning controls.
- ✓
Set a custom quota for concurrent queries
Why this is correct
A custom quota capping concurrent queries prevents runaway ad-hoc queries from monopolising slots and exhausting capacity. It satisfies the cost-control requirement by throttling query concurrency, so a single user or workload cannot overwhelm the project's shared BigQuery resources.
- ✓
Partition and cluster tables to reduce bytes processed
Why this is correct
Partitioning and clustering restrict each query to relevant data blocks, so BigQuery scans fewer bytes and charges less. This satisfies the cost-control requirement by reducing bytes processed, the metric on which on-demand query pricing is calculated.
- ✗
Create materialized views for all reporting queries
Why it's wrong here
Materialised views cache results for repeated identical queries, but ad-hoc analytical queries will not match them, so runaway scans remain unbilled-controlled. They suit accelerating predictable, recurring reporting workloads; cost control requires maximum bytes billed, custom quotas or reservations.
- ✓
Use BigQuery reservations (flex slots) for predictable workloads
Why this is correct
Reservations assign dedicated slot capacity to predictable workloads, decoupling their cost from bytes scanned and shielding them from ad-hoc query contention. This satisfies the requirement by giving stable, forecastable pricing for reporting workloads while autoscaling flex slots absorb peaks.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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