Your food ordering portal had an incident last week. You would like to offer a discount to 100 restaurants who encountered

To identify the 100 restaurants with the most errors, you must first filter the traces to the relevant context: the specific service, the ordering resource, and only traces marked as errors. Then, to count errors per restaurant, you group the results by the restaurant name tag. Finally, visualizing this data as a "Top List" and limiting it to 100 entries directly answers the request by showing the restaurants with the highest error counts in descending order.

C. Grouping by HTTP Status Code would show the most frequent error codes, not the restaurants that experienced them most often.

D. Grouping by service and resource name would aggregate errors by code function, not by the affected restaurant entity.

1. Datadog Documentation, "Trace Explorer". In the "Analytics" section, it describes the process of creating queries, grouping by facets (tags), and using visualizations. The "Top List" visualization is explicitly for ranking items by a measure. (See section: "Visualize trace analytics").

2. Datadog Documentation, "Search and Analyze". This page details how to build a query by filtering on services and resources, then using the "Group by" field with a tag like restaurant name to aggregate results.