Real-time sensor dashboard in Raspberry Pi - Practice Problems & Coding Challenges

Challenge - 5 Problems

🎯 Scenario

intermediate

Identify the best data refresh strategy for a real-time sensor dashboard

You are building a dashboard on a Raspberry Pi to display temperature sensor data in real-time. The sensor updates every second. Which data refresh strategy ensures the dashboard shows the latest data without overloading the device?

A Refresh the entire dashboard every second by reloading all data and visuals.

B Use a streaming data connection to update only the temperature value every second.

C Refresh the dashboard once every minute to reduce CPU usage.

D Manually refresh the dashboard only when the user clicks a button.

💡 Hint

Think about updating only what changes frequently to save resources.

Streaming data updates only the changing value, which is efficient for real-time sensor data on limited hardware like Raspberry Pi.

❓ dax_lod_result

intermediate

Calculate average temperature over last 5 minutes using DAX

Given a sensor data table with columns Timestamp and Temperature, which DAX measure correctly calculates the average temperature for the last 5 minutes?

Raspberry Pi

AverageTempLast5Min = CALCULATE(AVERAGE(SensorData[Temperature]), FILTER(SensorData, SensorData[Timestamp] >= NOW() - TIME(0,5,0)))

A AverageTempLast5Min = CALCULATE(AVERAGE(SensorData[Temperature]), SensorData[Timestamp] > NOW() - 5)

B AverageTempLast5Min = AVERAGE(SensorData[Temperature]) WHERE SensorData[Timestamp] >= NOW() - TIME(0,5,0)

C AverageTempLast5Min = CALCULATE(AVERAGE(SensorData[Temperature]), FILTER(SensorData, SensorData[Timestamp] >= NOW() - TIME(0,5,0)))

D AverageTempLast5Min = AVERAGE(FILTER(SensorData, SensorData[Timestamp] >= NOW() - TIME(0,5,0))[Temperature])

💡 Hint

Use CALCULATE with FILTER and NOW() minus 5 minutes.

Option C correctly uses CALCULATE with FILTER and NOW() - TIME(0,5,0) to filter last 5 minutes.

❓ visualization

advanced

Choose the best visualization for showing sensor temperature trends over time

You want to show how temperature changes every second over the last 10 minutes on your dashboard. Which visualization type is best for this purpose?

A A pie chart showing percentage of time temperature was in different ranges.

B A bar chart showing average temperature per minute.

C A scatter plot showing temperature vs humidity.

D A line chart showing temperature on Y-axis and time on X-axis.

💡 Hint

Think about showing continuous data over time.

A line chart is best for showing continuous changes over time, like temperature every second.

🔧 Debug

advanced

Identify the error in this DAX measure for latest sensor reading

Given this DAX measure to get the latest temperature reading:
LatestTemp = MAXX(FILTER(SensorData, SensorData[Timestamp] = MAX(SensorData[Timestamp])), SensorData[Temperature])
What is the problem with this measure?

A It returns blank because MAX inside FILTER does not evaluate correctly in row context.

B It returns the maximum temperature instead of the latest temperature.

C It causes a circular dependency error because MAX is used inside FILTER on the same column.

D It returns the latest timestamp but not the temperature value.

💡 Hint

Consider how MAX inside FILTER behaves in row context.

Using MAX inside FILTER on the same column causes context issues, so FILTER returns no rows and the measure returns blank.

❓ data_modeling

expert

Design a data model for multiple sensors with real-time updates

You have multiple sensors sending temperature and humidity data every second. You want to build a dashboard on Raspberry Pi that shows each sensor's latest readings and historical trends. Which data model design is best?

A A table with SensorID and latest readings only, plus a separate archive table for historical data.

B Separate tables per sensor, each with Timestamp, Temperature, Humidity columns; join them in the dashboard.

C One large table with columns: SensorID, Timestamp, Temperature, Humidity; update this table every second with new rows.

D Store all data in a single JSON file updated every second and parse it in the dashboard.

💡 Hint

Think about efficient queries for latest and historical data separately.

Option A separates latest readings for quick access and archives historical data, optimizing performance on limited hardware.