Google ACE Planning and Configuring a Cloud Solution Practice Question
An organization needs to run a batch analytics job on BigQuery every night that processes terabytes of data. The job is critical and must complete within a specific time window. To optimize costs, they are considering using flat-rate pricing but want to minimize commitment risk. Which THREE factors should they evaluate?
⚠ Common exam trap
Google Cloud often tests the misconception that storage infrastructure (like Cloud Storage buckets) or unrelated compute services (like Compute Engine) influence BigQuery pricing decisions, when in fact the focus should be on slot allocation and cost comparison with on-demand pricing.
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
✓
The cost of on-demand query pricing for the same workload
The organization should evaluate B, the cost of on-demand query pricing for the same workload, because comparing on-demand BigQuery pricing against flat-rate commitment costs is the baseline needed to determine whether a reservation actually saves money for this nightly terabyte-scale job. They should also evaluate D, the number of slots needed to complete the job within the required time window, since flat-rate capacity is measured in slots and the reservation size must be sufficient to finish the batch analytics within the critical window. They should evaluate E, the availability of flex slots for short-term capacity needs, because flex slots provide a low-commitment, short-duration way to obtain dedicated BigQuery capacity, which directly addresses the goal of minimizing commitment risk. Options A and C do not belong: the number of Cloud Storage buckets used for staging is irrelevant to BigQuery flat-rate slot commitment decisions, and reserving dedicated Compute Engine hardware is a different compute service and does not address BigQuery slot capacity or pricing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The number of Cloud Storage buckets used for data staging
Why it's wrong here
Cloud Storage buckets hold staged data and incur storage and request charges regardless of BigQuery pricing model, so bucket count does not affect flat-rate commitment sizing. It is tempting because staging costs appear in the same bill, but the decision hinges on slot demand, not bucket quantity.
- ✓
The cost of on-demand query pricing for the same workload
Why this is correct
Comparing on-demand query pricing for the identical workload establishes the break-even baseline: if on-demand costs less than the flat-rate commitment, committing wastes money. This directly addresses minimising commitment risk by quantifying whether reserved capacity is justified for the nightly batch job.
- ✗
The cost of reserving dedicated hardware for Compute Engine
Why it's wrong here
Flat-rate pricing buys BigQuery slots, not Compute Engine virtual machines, so reserving Compute Engine hardware is unrelated to the commitment. It is tempting because both are compute reservations with commitment discounts, but that mechanism applies to GCE instances, not BigQuery slot capacity.
- ✓
The number of slots needed to complete the job within the required time window
Why this is correct
Slot count determines whether the job finishes inside its required time window, since BigQuery throughput scales with available slots. Sizing this correctly identifies the minimum flat-rate commitment needed, preventing over-purchasing capacity and thereby minimising commitment risk.
- ✓
The availability of flex slots for short-term capacity needs
Why this is correct
Flex slots provide short-term, commitment-free capacity purchasable by the minute, letting the organisation cover the nightly batch window without a long-term flat-rate commitment. This directly satisfies the minimise-commitment-risk constraint while still guaranteeing the slots needed to meet the deadline.
Go deeper
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Key term
Organization
An Organization is a top-level container in Google Cloud that represents your company or entities and serves as the root node for all your cloud resources, policies, and access control.
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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This ACE 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 ACE exam.