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Professional Data Engineer Exam - Question 287


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?

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Correct Answer: C

To help the marketing team establish a consistent BigQuery analytics spend each month, you should create a BigQuery reservation with a baseline of 500 slots with no autoscaling. This setup ensures that the marketing team will have a predictable and fixed monthly cost for their BigQuery usage, avoiding the variability associated with the on-demand billing model. By having 500 slots reserved, the team can perform their analytics tasks without worrying about sudden cost spikes due to autoscaling or exceeding query quotas.

Discussion

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raaadOption: C
Jan 10, 2024

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.

AllenChen123
Jan 21, 2024

Why 500 slots?

AllenChen123
Jan 25, 2024

But seems only C makes sense. https://cloud.google.com/bigquery/quotas#query_jobs "There is no limit to the number of bytes that can be processed by queries in a project."

datapassionate
Jan 29, 2024

"However, you can set limits on the amount of data users can query by creating custom quotas to control query usage per day or query usage per day per user." https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas B would be correct

saschak94
Feb 9, 2024

If you use B - the marketing team wouldn't be able to run their queries when the quota is reached, which could harm the business. Having a reservation for 500 slots and no autoscaling gives you exact predictable cost for each month without harming the business or have variable cost with autoscaling So C should be the right answer

Sofiia98Option: B
Jan 10, 2024

https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas

lipa31Option: D
Jan 23, 2024

anybody for D ? https://cloud.google.com/bigquery/docs/slots-autoscaling-intro

MissK1371Option: B
Apr 24, 2024

at first I thought C for best practices but the questions does not ask to lower the cost just to make the spend consistent

LaxmanTiwari
Apr 26, 2024

u spot on MissK1371

tibuenocOption: D
Feb 2, 2024

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.

JyoGCP
Feb 21, 2024

Looks like only the baseline slots are guaranteed and immediately available for use where as autoscaling slots are not guaranteed immediately.

JyoGCP
Feb 21, 2024

So C is a better option

JyoGCP
Feb 21, 2024

which will give a consistent bill per month

ML6Option: C
Feb 18, 2024

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.

Anudeep58Option: B
May 18, 2024

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

Anudeep58
Jun 16, 2024

C. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly. This option provides the marketing team with a predictable monthly cost by reserving a fixed number of slots, ensuring that they have dedicated resources without the variability introduced by autoscaling or on-demand pricing. This setup also simplifies budgeting and financial planning for the marketing team, as they will have a consistent expense each month.

scaenruyOption: C
Jan 4, 2024

C. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.

Sofiia98
Jan 10, 2024

Provide, please, the reference

BigDataBBOption: B
Apr 11, 2024

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

virat_kohliOption: C
May 23, 2024

C. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.

fitri001Option: C
Jun 17, 2024

C. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.

8ad5266Option: B
Jun 27, 2024

Why 500 slots?

987af6bOption: A
Jul 21, 2024

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.