How to establish consistent BigQuery spend?
You are administering shared BigQuery datasets that contain views used by multiple teams in your organization. The marketing team is concerned about the variability of their monthly BigQuery analytics spend using the on-demand billing model. You need to help the marketing team establish a consistent BigQuery analytics spend each month. What should you do?
Community Votes
40% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
This question tests the ability to distinguish between cost capping (quotas) and cost predictability (fixed reservations), with the trap being selecting a quota option that limits spend but fails to ensure consistency.
To stabilize monthly BigQuery analytics costs, switching from the on-demand billing model to a fixed-slot reservation model is the recommended approach. The community consensus favors purchasing a fixed number of slots without autoscaling to ensure a flat, predictable monthly bill.
Many users select Option B (Quota) because it directly addresses 'spend,' but quotas only limit maximum usage rather than guaranteeing a fixed monthly expenditure.
Community Discussion (32 comments)
raaad
👍 13
Selected: C
Reservations guarantee a fixed number of slots (computational resources) for BigQuery queries, ensuring a predictable monthly cost, addressing the marketing team's concern about variability.
desertlotus1211
👍 1
Selected: C
Answer A - Autoscaling introduces variable costs — which defeats the goal of cost consistency Answer B: it doesn’t convert to predictable costs — on-demand billing still applies per scan. Answer C is best.
MarcoPellegrino
👍 1
Selected: B
The input doesn't specify the consistent monthly spent. hence, A, C, and D can't be used
Augustax
👍 1
Selected: A
Estimate a consistent spending doesn't mean overpay...
Maxd
👍 1
A because allow flexibility and scaling, so setting a baseline with autoscaling ensures that the marketing team can handle their queries without large fluctuations in cost.
b3e59c2
👍 1
Selected: C
C seems much more robust and reliable than B. We can keep spend consistent whilst not sacrificing on performance (if we do B, once the byte scan limit has been reached, users will not be able to perform any analysis which could be detrimental to business)
himadri1983
👍 2
Selected: C
This is trick question. The answer B is setting quota on bytes but it does not address the cost variability. The C will give the predictable monthly cost.
m_a_p_s
👍 1
Selected: C
Answer appears to be C. Check the example from docs: https://cloud.google.com/bigquery/docs/reservations-workload-management#managing_your_workloads_and_departments_using_reservations
CloudAdrMX
👍 1
It's a treaking question but it's C, they are asking for establish a consistent Bigquery analytics spend each month, if you put 500 slots as baseline and with no autoscaling, each month they'll get the a consistent Bigquery analytics spend.
cloud_rider
👍 1
Selected: B
A, C and D talks about slot counts, whereas question does not talk about any such requirement, we should not make assumption on slots required or not required. Option B provides the visibility of cost to the team and can be revised as needed. So B is the right option.
8284a4c
👍 4
Selected: A
The correct answer is: A. Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly. Here's the rationale: Consistent Spend with Reservation: Creating a BigQuery Enterprise reservation provides the marketing team with dedicated slots, which can help stabilize and predict their monthly costs. By having a reservation baseline of 250 slots, they are guaranteed a certain level of performance and cost each month. Autoscaling for Flexibility: The autoscaling up to 500 slots allows the team to handle spikes in demand without being constrained by the fixed slot count. Autoscaling in this scenario enables some flexibility while still providing predictable spending due to the baseline. Billing Back: The reservation model allows for internal chargeback by department based on slot usage, helping the marketing team plan a predictable budget.
mi_yulai
👍 2
Answer is B. Custom quotas are a powerful feature that allow you to set hard limits on specific resource usage. In the case of BigQuery, quotas allow you to control query usage (number of bytes processed) at a project- or user-level. Project-level custom quotas limit the aggregate usage of all users in that project, while user-level custom quotas are separately applied to each user or service account within a project. Custom quotas are relevant when you are using BigQuery’s on-demand pricing model, which charges for the number of bytes processed by each query. When you are using the capacity pricing model, you are charged for compute capacity (measured in slots) used to run queries, so limiting the number of bytes processed is less useful. By setting custom quotas, you can control the amount of query usage by different teams, applications, or users within your organization, preventing unexpected spikes in usage and costs.
baimus
👍 1
Selected: C
Just to clarify a point of confusion: setting a quota does not affect variability (as specified in the question). It means there is a limit to the maximum but it can still vary anywhere between zero and that maximum each month. It would also prevent the marking team actually performing the queries if set too low. C is the only one that makes sense, though the question "why 500" is a valid one, all the other answers simply do not deliver the requirements.
Preetmehta1234
👍 2
Selected: B
It should be B C is a subset of B. I mean you can put custom quota == 500 slots and obviously there wont be any auto scaling. that exactly the purpose of quota
chrissamharris
👍 1
A Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly. GIves a consistent baseline cost and allocation for peak times
Preetmehta1234
👍 3
Selected: B
The objective here is not performance. It's more concerned about the spend each month. It's not about 250 slots or 500 slots. Selecting a custom quota will let you select what ever slot you want but stay consistent with it, rather than getting stick by a particular slot option.
Preetmehta1234
👍 2
Selected: B
Custom Quota If you have multiple BigQuery projects and users, you can manage costs by requesting a custom quota that specifies a limit on the amount of query data processed per day. Daily quotas are reset at midnight Pacific Time. Custom quota is proactive, so you can't run an 11 TB query if you have a 10 TB quota. Creating a custom quota on query data lets you control costs at the project level or at the user level. Project-level custom quotas limit the aggregate usage of all users in that project.
4a8ffd7
👍 3
Selected: A
I think is A
viciousjpjp
👍 3
Selected: A
Balance of Cost and Performance: By setting a baseline of slots to ensure a minimum cost, and enabling autoscaling for flexible scaling based on demand, we can achieve an optimal balance between cost and performance. Cost Control: Setting a maximum number of slots allows us to clearly define a cost ceiling. Benefits of Enterprise Reservations: Enterprise reservations offer a higher discount rate compared to Standard reservations, and provide more stable performance.
meh_33
👍 2
Selected: C
Yes, you can reserve slots in BigQuery for on-demand billing: Slot recommender Use the slot recommender to get cost-optimized recommendations for on-demand workloads. Capacity commitment Purchase a capacity commitment to reserve capacity for a minimum amount of time and save on costs. Capacity-based pricing Use reservations to switch to capacity-based pricing, which lets you reserve a volume of slots. You pay for that capacity continuously every second it's deployed.
987af6b
👍 3
Selected: A
Explanation: Predictability: The baseline of 250 slots ensures a predictable minimum spend each month. Flexibility: Autoscaling up to 500 slots allows the team to handle peak workloads without interruptions. Balanced Cost: While Option B limits daily spend, it can lead to disruptions in service. Option A offers a consistent monthly cost while still accommodating variable workloads efficiently.
8ad5266
👍 2
Selected: B
Why 500 slots?
fitri001
👍 1
Selected: C
C. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.
virat_kohli
👍 1
Selected: C
C. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.
Anudeep58
👍 4
Selected: B
The question clearly mentions, that team is using the on-demand billing mode in BiqQuery, which charges for the number of bytes processed by each query. So limiting the bytes processed will be the solution. https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas
MissK1371
👍 3
Selected: B
at first I thought C for best practices but the questions does not ask to lower the cost just to make the spend consistent
BigDataBB
👍 1
Selected: B
As wrote @Sofia98 the company using "on-demand billing model", so the best solution should be the B, https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas
ML6
👍 2
I agree that at first sight option C seems best. However, the question mentions that they are currently on the on-demand billing model and option C does not mention anything about changing the pricing model from on-demand to capacity computing (BigQuery Standard or Enterprise edition). I don't believe it is possible to reserve slots with on-demand billing.
tibuenoc
👍 3
Selected: D
Option D - https://cloud.google.com/bigquery/pricing Standard Pay-as-you-go Reservation: This model charges only for the slots used, aligning with the marketing team's need for predictable costs. On-demand pricing would lead to variable costs, while committed use discounts or reservations with fixed costs wouldn't provide the needed flexibility. Baseline of 0 Slots: Setting a baseline of 0 ensures no upfront commitment and avoids unused capacity charges if the marketing team's usage is lower than expected. Autoscaling Up to 500 Slots: Autoscaling provides the flexibility to handle unexpected spikes in usage without incurring on-demand pricing costs. The 500-slot limit sets a reasonable upper bound to control spending. Billing Back: Billing the marketing team based on their actual usage promotes cost awareness and encourages responsible resource utilization.
lipa31
👍 3
anybody for D ? https://cloud.google.com/bigquery/docs/slots-autoscaling-intro
Sofiia98
👍 4
Selected: B
https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas
scaenruy
👍 1
Selected: C
C. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.
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Expert Analysis
Why the Answer Is Correct
Option C is correct because purchasing a reservation with a fixed baseline and no autoscaling converts the variable cost model (pay-per-byte) into a fixed cost model (pay-per-slot). By committing to 500 slots, the marketing team pays the same amount every month regardless of query volume, provided they stay within capacity. This directly addresses the requirement for 'consistent' monthly spend, eliminating the variability inherent in the on-demand model.Why the Other Options Are Wrong
Option A is incorrect because enabling autoscaling allows costs to fluctuate based on workload demand, violating the consistency requirement. Option B is incorrect because while custom quotas limit maximum daily bytes processed (preventing overages), they do not establish a consistent spend; costs can still vary significantly from month to month based on actual usage. Option D is incorrect because a 'Standard pay-as-you-go reservation' with a baseline of 0 essentially operates like on-demand pricing, where you pay only for what you use, resulting in variable costs.Community Comment Notes
Commenters supporting Option C emphasize that reservations guarantee a fixed number of slots, which ensures predictable monthly costs. Those supporting Option B argue that limiting bytes scanned controls spend, but as noted in other comments, this addresses cost caps rather than consistency. There is also debate regarding the specific reservation types, but the consensus leans towards the fixed capacity model for stability.Official Reference
Exam Strategy
When asked about 'consistent' or 'predictable' spend in cloud exams, look for options that convert variable pricing (pay-as-you-go) into fixed pricing (reservations, committed use discounts), and avoid options involving autoscaling or quotas that allow costs to fluctuate.