KPI dashboards in SCADA systems - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ dax_lod_result

intermediate

Calculate Total Downtime for a Specific Machine

You have a table MachineEvents with columns MachineID, EventType, and DurationMinutes. You want to calculate the total downtime for MachineID 101 using a DAX measure.

Which DAX expression correctly calculates this total downtime?

A TotalDowntime = CALCULATE(SUM(MachineEvents[DurationMinutes]), FILTER(MachineEvents, MachineEvents[MachineID] = 101 && MachineEvents[EventType] = "Downtime"))

B TotalDowntime = CALCULATE(SUM(MachineEvents[DurationMinutes]), MachineEvents[MachineID] = 101 && MachineEvents[EventType] = "Downtime")

C TotalDowntime = SUMX(FILTER(MachineEvents, MachineEvents[MachineID] = 101 && MachineEvents[EventType] = "Downtime"), MachineEvents[DurationMinutes])

D TotalDowntime = SUM(MachineEvents[DurationMinutes]) WHERE MachineEvents[MachineID] = 101 AND MachineEvents[EventType] = "Downtime"

💡 Hint

Use CALCULATE with a FILTER to apply multiple conditions.

Option A correctly uses CALCULATE with a FILTER to sum DurationMinutes only for rows where MachineID is 101 and EventType is "Downtime". Option A is invalid because filter conditions inside CALCULATE require FILTER or column filters, not direct logical expressions. Option A works but is less efficient. Option A is invalid syntax.

❓ visualization

intermediate

Best Visualization for Machine Efficiency Over Time

You want to show the efficiency of a machine over the past 12 months on a KPI dashboard. Efficiency is a percentage value updated monthly.

Which visualization type is best to clearly show trends and monthly changes?

A A pie chart showing efficiency percentages for each month

B A line chart with months on the x-axis and efficiency percentage on the y-axis

C A stacked bar chart showing efficiency and downtime side by side

D A scatter plot with efficiency on the x-axis and months on the y-axis

💡 Hint

Think about how to best show changes over time.

A line chart is ideal for showing trends over time because it connects data points in order, making it easy to see increases or decreases. Pie charts are not good for time series. Stacked bar charts can be confusing for percentages over time. Scatter plots do not show time progression clearly.

❓ data_modeling

advanced

Designing a Data Model for Multi-Machine KPI Dashboard

You need to build a KPI dashboard that shows downtime, production count, and efficiency for multiple machines. Data comes from separate tables: Machines, Events, and Production. Which data modeling approach is best to enable easy KPI calculations and filtering by machine?

A Merge all tables into one large table with all columns combined

B Keep tables separate without relationships and use LOOKUPVALUE in measures

C Create relationships between <code>Machines[MachineID]</code> and <code>Events[MachineID]</code>, and between <code>Machines[MachineID]</code> and <code>Production[MachineID]</code>, then use <code>Machines</code> as a filter table

D Create a single table with only machine names and aggregate all data in measures without relationships

💡 Hint

Think about how relationships help filtering and calculations.

Option C is best practice: using a Machines table as a dimension connected to fact tables Events and Production allows easy filtering and aggregation. Merging all data (B) can cause redundancy and performance issues. Using LOOKUPVALUE (C) is less efficient and complex. Option C loses detail and filtering ability.

🔧 Debug

advanced

Identify the Error in a DAX Measure for Average Production

Given this DAX measure to calculate average production per day:

AvgProduction = AVERAGE(Production[Count])

It returns incorrect results when filtered by machine and date. What is the likely cause?

A The measure does not consider the date context, so averages over all data ignoring filters

B The <code>AVERAGE</code> function cannot be used on numeric columns

C The <code>Production[Count]</code> column contains text values causing errors

D The measure should use <code>SUM</code> instead of <code>AVERAGE</code> to get correct results

💡 Hint

Think about how context affects aggregation in DAX.

Option A is correct because AVERAGE respects filter context, but if the data model or filters are not set properly, the measure might ignore date or machine filters. The issue is usually missing relationships or incorrect filter context, not the function itself. Option A is false; AVERAGE works on numeric columns. Option A changes the aggregation type incorrectly. Option A is unlikely if data is numeric.

🧠 Conceptual

expert

Optimizing KPI Dashboard Performance with Large Data

Your KPI dashboard slows down significantly when loading data from millions of SCADA system events. Which strategy will best improve performance without losing important KPI accuracy?

A Remove all filters and show only total KPIs to reduce calculation time

B Load all raw event data into the dashboard and apply filters dynamically at runtime

C Use complex calculated columns on raw data tables to compute KPIs on the fly

D Implement data aggregation tables that pre-calculate daily summaries for KPIs and use them in the dashboard

💡 Hint

Think about pre-processing data to reduce load.

Option D is best because pre-aggregating data reduces the volume the dashboard processes, improving speed while keeping KPI accuracy. Option D causes slowdowns due to large data volume. Option D loses detail and usefulness. Option D increases calculation complexity and slows performance.