Google PCA Practice Question: Analyze and optimize technical and business processes
A logistics company has a BigQuery dataset that is queried heavily by scheduled reports each morning. Finance wants predictable monthly spend and the ability to attribute query cost to each department. Analysts currently run ad hoc queries against on-demand pricing, and costs vary widely month to month. What should the architect recommend?
⚠ Common exam trap
The trap here is choosing a quota or a deprecated flat-rate purchase when the requirement is predictable spend plus per-department attribution, which reservations and assignments provide.
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 separate project per department and enable BigQuery reservations with slot commitments assigned to each project.
Reservations with committed slots turn variable on-demand query charges into a fixed, forecastable monthly figure, and assigning capacity per project makes each department's usage attributable. This satisfies the finance requirement directly, whereas quotas merely cap usage and alternative engines add complexity without solving the predictability and attribution goals.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set a custom quota on bytes billed per day for each department's service account and let queries fail when the quota is reached.
Why it's wrong here
Quotas cap spend but do not make it predictable, and failing queries disrupts the morning reports that the business depends on. It also does not provide a clean per-department cost signal, since quota consumption is a limit rather than an allocation, and ad hoc queries would simply error out instead of drawing on reserved capacity.
- ✓
Create a separate project per department and enable BigQuery reservations with slot commitments assigned to each project.
Why this is correct
BigQuery reservations with committed slots convert variable on-demand query charges into a fixed monthly cost, and assigning reservations per project gives each department an attributable capacity pool. This matches the finance requirement for predictability and per-department attribution, while scheduled reports draw from dedicated slots instead of competing for shared on-demand capacity.
- ✗
Enable the BigQuery flat-rate legacy pricing model by purchasing a fixed number of slots per project.
Why it's wrong here
The legacy flat-rate model has been superseded by reservations and commitments, and per-project slot purchases do not match the current capacity management model. Recommending a deprecated purchasing path risks an unsupported configuration and does not deliver the clean reservation-based attribution that reservations with assignments provide.
- ✗
Move the dataset to Cloud Bigtable and run the scheduled reports with a Dataflow job each morning.
Why it's wrong here
Bigtable is a wide-column store tuned for high-throughput key lookups, not for the analytical SQL aggregations the reports require. Replacing BigQuery with Bigtable plus Dataflow adds engineering work, loses the serverless SQL model, and does not by itself deliver the fixed monthly cost or department-level attribution finance asked for.
Go deeper
Related to this question
Learn chapter
Billing, Budgets, and Cost Management
Key term
Project
A project is a temporary endeavor with a defined beginning and end, undertaken to create a unique product, service, or result, managed through specific processes in IT environments.
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
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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
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