The correct approach is to store the model in Object Storage and reference it in the deployment configuration, as this bypasses the 10 GB artifact size limit entirely. OCI Data Science model deployments enforce a hard 10 GB cap on the uploaded artifact, but by placing the model files—such as a 12 GB fine-tuned Llama 2—in Object Storage and specifying the URI in the deployment configuration, the service loads the model at runtime without needing the artifact within the deployment package. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of OCI Data Science’s architectural boundaries and the practical workaround for large generative AI models. A common trap is attempting to compress or split the artifact, which fails because the limit is on the uncompressed upload size. Remember the memory tip: “Object Storage is the overflow valve for the 10 GB limit.”
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 machine learning engineer is deploying a fine-tuned Llama 2 model on OCI Data Science model deployment. The deployment fails with an error: 'Model artifact exceeds the maximum allowed size of 10 GB.' The model files total 12 GB. What is the best approach to resolve this?
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
✓
Store the model in Object Storage and reference it in the deployment configuration
Option A is correct because OCI Data Science model deployment has a hard limit of 10 GB for the model artifact uploaded directly. By storing the model in Object Storage and referencing it in the deployment configuration, you bypass this limit entirely, as the deployment service can load the model from Object Storage at runtime without requiring the artifact to be part of the deployment package.
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.
✓
Store the model in Object Storage and reference it in the deployment configuration
Why this is correct
Object Storage allows large models and is supported by model deployment.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a different model that is smaller than 10 GB
Why it's wrong here
Not a solution if the specific model is required.
✗
Increase the model deployment artifact size limit via a service request
Why it's wrong here
The limit is fixed and cannot be increased.
✗
Compress the model artifact to under 10 GB using gzip
Why it's wrong here
Compression may not achieve <10 GB and deployment may not support compressed format.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that you can increase service limits via a support ticket, but for model artifact size, the limit is architectural and not adjustable; candidates may also incorrectly assume compression solves the issue without considering decompression at runtime.
Detailed technical explanation
How to think about this question
Under the hood, OCI Data Science model deployment uses a containerized environment where the model artifact is extracted into the container's filesystem. Object Storage integration allows the deployment to mount or download the model at startup using pre-signed URLs or service principals, keeping the deployment package small. In real-world scenarios, large models like Llama 2 (7B, 13B, 70B) often exceed 10 GB, making Object Storage the standard approach for production deployments.
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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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: Store the model in Object Storage and reference it in the deployment configuration — Option A is correct because OCI Data Science model deployment has a hard limit of 10 GB for the model artifact uploaded directly. By storing the model in Object Storage and referencing it in the deployment configuration, you bypass this limit entirely, as the deployment service can load the model from Object Storage at runtime without requiring the artifact to be part of the deployment package.
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.
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