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A sales manager wants to ask Genie for last quarter's total revenue by product category, displayed as a table. Which SPARK question is most likely to produce the right result?
Show me revenue.
What was total revenue in dollars by product category for Q2 2026, shown as a table?
Give me the latest revenue numbers with as much detail as possible.
You ask Genie a question and the answer looks unexpectedly low. After checking the source citation, you notice Genie filtered to 'active' customers only. You expected results for all customers. What is the best next step?
Accept the answer because Genie's filters are always correct.
Dismiss the answer and wait for a human analyst to run the report.
Ask a follow-up question that explicitly includes all customers, for example: 'What was total revenue for Q2 2026 for all customers, including inactive ones, shown as a table?'
A colleague mentions Genie told them 'why the Southern region underperformed in Q1.' Should you rely on that interpretation in a formal report?
Yes—Genie's causal analysis is verified against the data, so it's reliable.
No—for standard data queries, Genie retrieves and reports data but doesn't analyze causes or give recommendations. You should treat any causal interpretation with skepticism and verify it with a domain expert.
Only if the answer is labeled Trusted.
You want to use Genie to answer a one-off data question during a broader Microsoft 365 Copilot conversation without switching to a dedicated agent session. Which method should you use?
Open a dedicated agent conversation via More agents and select Databricks Genie.
Type @Databricks and select Databricks Genie in a new Microsoft 365 Copilot chat.
Open the Databricks Genie app directly in Azure Databricks.
You're working in a high-traffic public Teams channel. A colleague mentions @Databricks Genie and shares a number from the answer in the channel. Who is accountable for decisions made based on that number?
Databricks, because Genie generated the answer.
The channel members who act on the number, because each person is accountable for their own decisions made from AI-generated answers.
No one—AI-generated answers have no accountability chain.
You must answer all questions before checking your work.
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