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Deploying and Managing Generative AI on OCIhardMultiple ChoiceObjective-mapped

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

A company deploys a large language model on a dedicated AI cluster with 4 nodes. The model requires 128 GB of memory per instance, but the nodes have only 64 GB each. During inference, the nodes experience out-of-memory errors. What is the best solution?

⚠ Common exam trap

Oracle often tests the misconception that scaling out (more nodes) or scaling down (batch size) can fix memory constraints for large models, but the trap here is that the model's parameter memory is fixed and cannot be reduced by batch size changes, and adding more nodes without parallelism still leaves each node unable to host the full model.

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

Enable model parallelism across nodes

Model parallelism splits the model's layers or parameters across multiple nodes, allowing the 128 GB model to be distributed across the 4 nodes (each with 64 GB) so that no single node exceeds its memory capacity. This is the best solution because it directly addresses the memory constraint without requiring additional hardware or sacrificing inference throughput, and it is a standard technique for deploying large language models on distributed AI clusters.

Answer analysis

Option-by-option breakdown

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

  • Enable model parallelism across nodes

    Why this is correct

    Model parallelism distributes the model across nodes, enabling inference with the available memory.

  • Increase the number of nodes to 8

    Why it's wrong here

    Increasing nodes does not increase per-node memory.

  • Upgrade to higher memory node shapes

    Why it's wrong here

    This is a possible solution but may not be feasible due to cost or availability.

  • Reduce the batch size in inference requests

    Why it's wrong here

    Batch size reduction may help with memory per request, but the model itself still requires more memory than available.

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