What can be calculated using the information contained in the run_result artifact?

The run_result artifact provides insights specifically related to the performance and success of dbt models during execution. Among its various outputs, it includes metrics such as average model runtime and test failure rates, which are crucial for performance monitoring and debugging within a dbt project. Analyzing these metrics enables analytics engineers to identify which models are performing optimally and which may require optimization or troubleshooting due to failures.

In contrast, while the other options may seem plausible, they do not align as closely with the specific metrics provided by the run_result artifact. For instance, total number of tables created, available resources, and peak memory usage may be relevant to a broader context but are not specifically captured or derived from the run_result artifact during dbt executions. Thus, focusing on average model runtime and test failure rates reflects the direct purpose of the run_result in analyzing dbt model performance.