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Labs

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.

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Lab platform
Lab Info
Level
Advanced
Last updated
Sep 01, 2026
Duration
45m

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Table of Contents
  1. 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.

  2. 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.

  3. Challenge

    Produce a Validation Artifact

    Verify that successful validation creates the expected validation.json artifact in Amazon S3.

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