The correct solution is to enable model parallelism across the nodes. This technique directly resolves the out-of-memory error by splitting the large model’s layers or parameters across all four nodes, so the 128 GB requirement is distributed among the 64 GB instances, preventing any single node from exceeding its memory limit. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of distributed inference strategies for large language models on dedicated AI clusters, where the common trap is to confuse model parallelism with data parallelism—data parallelism replicates the model, which would still exceed memory, while model parallelism partitions it. Remember the memory tip: “Split the model, not the data” to avoid out-of-memory errors on OCI Generative AI.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
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.
✓
Enable model parallelism across nodes
Why this is correct
Model parallelism distributes the model across nodes, enabling inference with the available memory.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Model parallelism in OCI AI clusters typically uses frameworks like PyTorch with FSDP (Fully Sharded Data Parallel) or Megatron-LM, which shard model parameters, gradients, and optimizer states across GPUs/nodes. Under the hood, each node holds only a fraction of the model weights, and during inference, all-to-all communication (e.g., via NCCL) aggregates partial results, enabling the model to run on aggregate memory that exceeds any single node's capacity. A real-world scenario is deploying GPT-3 (175B parameters) across multiple nodes where each node has limited GPU memory, requiring tensor parallelism and pipeline parallelism to avoid OOM errors.
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: 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.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This 1Z0-1127 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.