OData and REST API connections in Power BI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding OData vs REST API in Power BI

Which statement correctly describes a key difference between OData and REST API connections in Power BI?

A OData connections only support XML format, whereas REST API supports JSON only.

B REST API automatically generates data models, but OData requires manual data shaping.

C OData provides a standardized queryable data feed, while REST API requires custom parsing of JSON responses.

D REST API connections in Power BI do not support authentication, but OData does.

💡 Hint

Think about how Power BI interacts with data formats and query standards.

OData is a standardized protocol that allows Power BI to query data feeds directly with filters and selections. REST APIs often return JSON that requires custom parsing and shaping.

❓ dax_lod_result

intermediate

DAX Measure for Counting API Calls by Date

Given a table 'ApiCalls' with columns 'CallDate' and 'Status', which DAX measure correctly counts the number of successful calls (Status = "Success") per day?

Power BI

Measure = CALCULATE(COUNTROWS(ApiCalls), FILTER(ApiCalls, ApiCalls[Status] = "Success"))

A Measure = COUNTROWS(FILTER(ApiCalls, ApiCalls[Status] = "Success"))

B Measure = CALCULATE(COUNTROWS(ApiCalls), FILTER(ApiCalls, ApiCalls[Status] = "Success"))

C Measure = COUNTROWS(ApiCalls[Status] = "Success")

D Measure = CALCULATE(COUNT(ApiCalls[Status]), ApiCalls[Status] = "Success")

💡 Hint

Remember that FILTER returns a table and CALCULATE modifies filter context.

Option B correctly uses CALCULATE with FILTER to count rows where Status is "Success". Option B is valid DAX and will work correctly. Option B is syntactically wrong. Option B misuses COUNT and filter syntax.

❓ visualization

advanced

Best Visualization for API Response Time Analysis

You have connected Power BI to a REST API that returns response times for each API call. Which visualization best helps identify slow response trends over time?

A Line chart showing average response time per day

B Pie chart showing percentage of calls per status

C Stacked bar chart showing count of calls by endpoint

D Table listing all API calls with response times

💡 Hint

Think about how to show trends over time clearly.

A line chart is ideal for showing trends in average response time over days, making it easy to spot slowdowns. Pie charts and stacked bars do not show trends over time. Tables show details but not trends visually.

🔧 Formula Fix

advanced

Debugging REST API Connection Failure

You set up a REST API connection in Power BI but get an authentication error. Which of the following is the most likely cause?

A The data returned is in JSON format instead of XML.

B The API endpoint URL is missing the query parameters.

C The Power BI desktop version is outdated.

D The API key was not included in the HTTP request headers.

💡 Hint

Authentication errors usually relate to missing or incorrect credentials.

Missing API key in headers causes authentication failure. Missing query parameters or data format issues cause different errors. Power BI version rarely causes authentication errors.

❓ data_modeling

expert

Modeling Data from Multiple OData Feeds

You have two OData feeds: 'Customers' and 'Orders'. Each feed is connected separately in Power BI. What is the best way to model these tables to analyze total sales per customer?

A Create a relationship between 'Customers[CustomerID]' and 'Orders[CustomerID]' and use a measure to sum order amounts.

B Append both tables into one combined table and sum sales directly.

C Use only the 'Orders' table since it contains customer IDs and sales data.

D Create a calculated column in 'Customers' that fetches order totals via API calls.

💡 Hint

Think about how relational data is modeled in Power BI.

Creating a relationship between the two tables on CustomerID allows you to analyze sales per customer efficiently. Appending tables mixes different entities. Using only Orders loses customer details. Calculated columns with API calls are inefficient and unsupported.