The answer is to select a node shape with higher GPU memory, such as 80 GB, because the model requires 64 GB of GPU memory but the dedicated AI cluster uses a single node with only 48 GB. OCI Generative AI managed endpoints require the entire model to fit on a single node, as distributed inference across multiple nodes is not supported, so increasing the number of nodes or enabling model parallelism will not resolve the insufficient GPU memory error. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this scenario tests your understanding of node shape constraints for model deployment, and a common trap is assuming you can add more nodes to pool memory. Remember the key rule: for managed endpoints, one node must host the whole model, so always match the node’s GPU memory to the model’s requirement. A helpful memory tip is “One node, one model—scale up, not out.”
1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data science team at a healthcare company has fine-tuned a Llama 2 model using OCI Data Science and registered it in the Model Catalog. They want to deploy it as a managed endpoint using OCI Generative AI. The model requires 64 GB of GPU memory. The team has created a dedicated AI cluster with a single node shape that has 48 GB GPU memory. When they attempt to deploy the model, the deployment fails with an error indicating insufficient resources. The team has verified that the model artifact is correct and that the compartment policies allow deployment. What should the team do to successfully deploy the model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Select a node shape with higher GPU memory, such as 80 GB.
Option C is correct because the model requires 64 GB of GPU memory, but the dedicated AI cluster uses a node shape with only 48 GB. The only way to satisfy the memory requirement is to select a node shape with higher GPU memory, such as 80 GB, as OCI Generative AI managed endpoints require a single node to host the entire model. Increasing nodes or enabling model parallelism does not help because OCI Generative AI does not support distributed inference across nodes for managed endpoints, and reducing precision may not guarantee the model fits or may degrade accuracy.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the number of nodes in the cluster to 2.
Why it's wrong here
More nodes do not increase per-node memory; the model still cannot fit on a single node.
✗
Enable model parallelism to split the model across nodes.
Why it's wrong here
Model parallelism requires multiple nodes and configuration; not immediately available.
✓
Select a node shape with higher GPU memory, such as 80 GB.
Why this is correct
Using a node shape with sufficient memory allows the model to be loaded.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the model's precision from FP16 to INT8 to lower memory usage.
Why it's wrong here
This may reduce memory but could degrade accuracy and is more complex.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think adding more nodes or enabling model parallelism can aggregate GPU memory, but OCI Generative AI managed endpoints do not support distributed inference across nodes, so the only valid solution is to use a node shape with sufficient single-GPU memory.
Detailed technical explanation
How to think about this question
OCI Generative AI managed endpoints use dedicated AI clusters where each node provides a fixed amount of GPU memory (e.g., 48 GB or 80 GB). The model must fit entirely within a single node's GPU memory because the service does not support multi-node inference or memory pooling. Reducing precision (e.g., from FP16 to INT8) can halve memory usage, but it may not always be feasible if the model was fine-tuned with specific precision requirements, and it can affect output quality. In practice, always match the node shape's GPU memory to the model's peak memory footprint, including overhead for activations and intermediate tensors.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A junior network technician can log in to a core router but cannot reach the enable prompt or configuration mode. The AAA server is authenticating the login — but the authorisation policy only grants privilege level 1, not 15. Authentication (who you are) is working; authorisation (what you can do) is not.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Select a node shape with higher GPU memory, such as 80 GB. — Option C is correct because the model requires 64 GB of GPU memory, but the dedicated AI cluster uses a node shape with only 48 GB. The only way to satisfy the memory requirement is to select a node shape with higher GPU memory, such as 80 GB, as OCI Generative AI managed endpoints require a single node to host the entire model. Increasing nodes or enabling model parallelism does not help because OCI Generative AI does not support distributed inference across nodes for managed endpoints, and reducing precision may not guarantee the model fits or may degrade accuracy.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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