You are designing a Power BI report that tracks hourly energy consumption. The operations team wants the visual to automatically highlight unexpected spikes or drops and display an AI-generated explanation when a point is selected. They insist on using only built-in visuals and no custom code. What should you do to meet the requirement?
Build a KPI visual against a target measure and configure data-driven alerts in the Power BI service.
Add a line chart with the datetime field on the X-axis, enable Find anomalies in the Analytics pane, and adjust the Sensitivity slider.
Create a clustered column chart, add an Average line, and use conditional formatting to change bar colors when values deviate.
Insert a decomposition tree visual, drill into the measure, and enable a Smart Narrative to provide explanations.
The correct approach uses a line chart with a date or time axis. By opening the Analytics pane and enabling Find anomalies, Power BI highlights outliers, provides a Sensitivity slider to tune detection, and generates AI explanations for each anomaly. Clustered column charts with conditional formatting merely recolor bars and never detect anomalies, decomposition trees focus on contribution analysis rather than outlier detection, and KPI alerts signal threshold breaches but do not create inline AI anomaly explanations.
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