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Use DAX Calculate function with filters

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskI need to show the total sales for the 'Electronics' category in Q4 2023. Apply a filter for…+
I need to show the total sales for the 'Electronics' category in Q4 2023. Apply a filter for that category and time period.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore I present this to Sarah in sales, make sure the Q4 2023 Electronics sales figure stands…+
Before I present this to Sarah in sales, make sure the Q4 2023 Electronics sales figure stands out. Can we also include a comparison to Q3 2023 right next to it, so she can easily see the trend?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe Q4 2023 Electronics sales numbers are much lower than expected, and I'm presenting to the…+
The Q4 sales numbers for Electronics look off compared to last year, and I'm presenting to the board in an hour.
The Q4 2023 Electronics sales numbers are much lower than expected, and I'm presenting to the board in an hour. I used the standard calculation for total sales with the category and date filters, but it's not aligning with the historical data I have. I'm afraid I've missed a subtle filter interaction or an inactive relationship that's skewing the total. What's the most likely reason for this discrepancy, and how can I quickly verify the calculation's accuracy before the presentation?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI keep getting caught off guard by unexpected data anomalies in my reports, especially when…+
I keep getting caught off guard by unexpected data anomalies in my reports.
I keep getting caught off guard by unexpected data anomalies in my reports, especially when applying multiple filters to key metrics for executive reviews. This wastes critical time trying to debug under pressure. What habit should I change to proactively catch these calculation issues before they become last-minute crises, especially when combining filters on different dimensions?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Microsoft Power BI's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.