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Deploying and Managing Generative AI on OCImediumMultiple ChoiceObjective-mapped

1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question

A data scientist is deploying a custom generative AI model using OCI Data Science. After deploying the model to an endpoint, they notice that inference requests are failing with a timeout error when the payload size exceeds 1 MB. What is the most likely cause and solution?

⚠ Common exam trap

Many exam-takers confuse a payload size limit with a generic timeout or resource issue, leading them to choose load balancer reconfiguration (A) or scaling (B) instead of recognizing the explicit payload limit enforced by the deployment configuration.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

The model deployment has a default payload size limit of ~1 MB; increase the payload limit in the deployment configuration.

OCI Data Science model deployments have a default payload size limit of approximately 1 MB. When inference requests exceed this limit, the load balancer or gateway times out the request. The solution is to increase the payload limit in the deployment configuration, which can be adjusted via the OCI console or API by modifying the `maximumRequestPayloadSize` setting.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The load balancer is misconfigured; reconfigure the load balancer timeout settings.

    Why it's wrong here

    Load balancer timeout may cause issues, but the primary cause is the payload limit on the model deployment.

  • The model server lacks sufficient memory; scale out to more instances.

    Why it's wrong here

    Memory may be an issue, but the error is specifically about payload size, not memory.

  • The model is not optimized for large payloads; use AutoML to optimize the model.

    Why it's wrong here

    AutoML is for model building, not for inference payload limits.

  • The model deployment has a default payload size limit of ~1 MB; increase the payload limit in the deployment configuration.

    Why this is correct

    OCI Data Science model deployments have a default request payload limit that can be increased.

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