Questão 1
OCI 2026 Architect Associate 1Z0-1072-26
Questão 1 — OCI 2026 Architect Associate 1Z0-1072-26
You have a AI/ML application running on Oracle Cloud Infrastructure. You identified that the application needs GPU and at least 20Gbps Network throughput. The application is currently using a VM.Standard2.1 compute without any block storage attached to it.
Which two options allow you to get your required performance for your application? (Select two)
Which two options allow you to get your required performance for your application? (Select two)
- A. Terminate the compute instance preserving the boot volume. Create a new compute instance using the BM.GPU2.2 shape using the boot volume preserved and attach a new block volume to host your application.
- B. Terminate the compute instance preserving the boot volume. Create a new compute instance Using the BM.GPU2.2 shape using the boot volume preserved, but no block volume attached.
- C. Terminate the compute instance preserving the boot volume. Create a new compute instance Using the VM.GPU3.4 shape using the boot volume preserved and use the NVMe devices to host your application.
Resposta correta:
A, C
Explicação
Most appropriate answers are:
Terminate the compute instance preserving the boot volume. Create a new compute instance using the BM.GPU2.2 shape using the boot volume preserved and attach a new block volume to host your application.
Terminate the compute instance preserving the boot volume. Create a new compute instance using the VM.GPU3.4 shape using the boot volume preserved and use the NVMe devices to host your application.
Explanation:
Both options provide the required GPU capability and the necessary network throughput for your AI/ML application. VM.GPU3.4 offers a higher performance with 8 NVIDIA V100 GPUs and 100 Gbps network bandwidth, BM.GPU2.2 while provides a more cost-effective solution with 2 NVIDIA P100 GPUs and 25 Gbps network bandwidth.
The current compute instance, VM.Standard2.1, is not suitable for GPU workloads or for delivering 20 Gbps+ network throughput. To meet the application's GPU requirement and high network performance, you need to migrate to a GPU-enabled compute shape.
✔ Option 1: BM.GPU2.2
BM (Bare Metal) GPU2.2 shapes provide NVIDIA GPUs, high throughput networking (>20 Gbps), and are designed for high-performance AI/ML workloads.
Preserving the boot volume allows reuse of the OS and application environment.
Attaching a new block volume ensures storage scalability and durability.
✔ Option 2: VM.GPU3.4
VM-based GPU shapes like GPU3.4 offer excellent performance with NVIDIA A100 GPUs and NVMe-based local storage, which is extremely fast for I/O-bound ML workloads.
NVMe can serve as high-performance storage for temporary datasets.
Preserving the boot volume ensures continuity.
Incorrect Option:
VM.Standard2.2 does not support GPUs and may not deliver the required 20 Gbps network performance.
Conclusion:To run GPU-intensive AI/ML workloads with high network demands on OCI, the best choices are GPU-enabled shapes like BM.GPU2.2 or VM.GPU3.4, using preserved boot volumes and appropriate high-performance storage.
Reference: https://docs.oracle.com/en-us/iaas/Content/Compute/References/computeshapes.htm
Terminate the compute instance preserving the boot volume. Create a new compute instance using the BM.GPU2.2 shape using the boot volume preserved and attach a new block volume to host your application.
Terminate the compute instance preserving the boot volume. Create a new compute instance using the VM.GPU3.4 shape using the boot volume preserved and use the NVMe devices to host your application.
Explanation:
Both options provide the required GPU capability and the necessary network throughput for your AI/ML application. VM.GPU3.4 offers a higher performance with 8 NVIDIA V100 GPUs and 100 Gbps network bandwidth, BM.GPU2.2 while provides a more cost-effective solution with 2 NVIDIA P100 GPUs and 25 Gbps network bandwidth.
The current compute instance, VM.Standard2.1, is not suitable for GPU workloads or for delivering 20 Gbps+ network throughput. To meet the application's GPU requirement and high network performance, you need to migrate to a GPU-enabled compute shape.
✔ Option 1: BM.GPU2.2
BM (Bare Metal) GPU2.2 shapes provide NVIDIA GPUs, high throughput networking (>20 Gbps), and are designed for high-performance AI/ML workloads.
Preserving the boot volume allows reuse of the OS and application environment.
Attaching a new block volume ensures storage scalability and durability.
✔ Option 2: VM.GPU3.4
VM-based GPU shapes like GPU3.4 offer excellent performance with NVIDIA A100 GPUs and NVMe-based local storage, which is extremely fast for I/O-bound ML workloads.
NVMe can serve as high-performance storage for temporary datasets.
Preserving the boot volume ensures continuity.
Incorrect Option:
VM.Standard2.2 does not support GPUs and may not deliver the required 20 Gbps network performance.
Conclusion:To run GPU-intensive AI/ML workloads with high network demands on OCI, the best choices are GPU-enabled shapes like BM.GPU2.2 or VM.GPU3.4, using preserved boot volumes and appropriate high-performance storage.
Reference: https://docs.oracle.com/en-us/iaas/Content/Compute/References/computeshapes.htm
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