- Lab
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Libraries: If you want this lab, consider one of these libraries.
- Cloud
Diagnosing and Correcting Unstable Bedrock Behavior
In this challenge lab, you will investigate an Amazon Bedrock-powered prototype that is operating outside its expected value boundaries. You will review previously collected operational results, identify the source of excessive token usage, and examine the prototype's predefined inference configurations to determine which operational control is responsible for the observed behavior. You will then apply a focused configuration correction and execute the same predefined workload using the corrected configuration. Finally, you will review the resulting validation data and verify that the prototype now operates within its defined token usage boundary. Successful validation produces a deterministic artifact in Amazon S3, providing measurable evidence that the correction restored expected operational behavior without relying on the quality of AI-generated responses.
Lab Info
Table of Contents
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Challenge
Correct the Prototype Configuration
Apply a focused configuration correction that restores the prototype to its expected operational boundary.
This is where we can potentially validate that the corrected configuration/profile is reflected in the resulting artifact.
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Challenge
Validate the Corrected Prototype
Execute the corrected prototype and verify that its operational behavior satisfies the defined validation requirements.
This is the strongest assertion target. Successful execution produces validation.json in S3, and that artifact contains the deterministic evidence we designed specifically for grading.
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Challenge
Produce a Validation Artifact
Verify that successful validation creates the expected validation.json artifact in Amazon S3.
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