- A
Fine-tune the model using OCI Generative AI service
Why wrong: Fine-tuning is a prior step; deployment steps assume model is ready.
- B
Create a model artifact (e.g., pickle, ONNX) with inference code
Model must be packaged with dependencies for serving.
- C
Register the model in OCI Generative AI service
Why wrong: Generative AI service is separate; custom models are deployed via Data Science.
- D
Upload the model artifact to an OCI Object Storage bucket
Model Deployment loads the artifact from Object Storage.
- E
Create a model deployment using the OCI Data Science Model Deployment service
This creates the endpoint.
Question 142 of 991
How to Deploy a Custom Generative AI Model on OCI Data Science
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.
Which THREE steps are required to deploy a custom generative AI model using OCI Data Science Model Deployment?
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
✓
Create a model artifact (e.g., pickle, ONNX) with inference code
Option B is correct because deploying a custom generative AI model via OCI Data Science Model Deployment requires packaging the model and its inference code into a standardized artifact format (e.g., pickle, ONNX). This artifact is the core input that the deployment runtime loads to serve predictions, making it an essential step in the workflow.
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.
- ✗
Fine-tune the model using OCI Generative AI service
Why it's wrong here
Fine-tuning is a prior step; deployment steps assume model is ready.
- ✓
Create a model artifact (e.g., pickle, ONNX) with inference code
Why this is correct
Model must be packaged with dependencies for serving.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Register the model in OCI Generative AI service
Why it's wrong here
Generative AI service is separate; custom models are deployed via Data Science.
- ✓
Upload the model artifact to an OCI Object Storage bucket
Why this is correct
Model Deployment loads the artifact from Object Storage.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create a model deployment using the OCI Data Science Model Deployment service
Why this is correct
This creates the endpoint.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing the OCI Generative AI service's managed model lifecycle (fine-tuning and registration) with the custom model deployment workflow in OCI Data Science, leading candidates to incorrectly select steps that belong to the managed service rather than the custom deployment pipeline.
Detailed technical explanation
How to think about this question
Under the hood, OCI Data Science Model Deployment expects a model artifact that includes a runtime.yaml file specifying the entry point and dependencies, along with the serialized model file (e.g., .pkl, .onnx). The deployment service pulls the artifact from Object Storage, spins up a managed container with the specified environment, and exposes a REST endpoint for inference, handling autoscaling and load balancing automatically.
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.
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Deploying and Managing Generative AI on OCI — study guide chapter
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
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: Create a model artifact (e.g., pickle, ONNX) with inference code — Option B is correct because deploying a custom generative AI model via OCI Data Science Model Deployment requires packaging the model and its inference code into a standardized artifact format (e.g., pickle, ONNX). This artifact is the core input that the deployment runtime loads to serve predictions, making it an essential step in the workflow.
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.
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 →
Same concept, more angles
2 more ways this is tested on 1Z0-1127
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE components are required to deploy a custom generative AI model on OCI Data Science model deployment?
hard- A.A load balancer to distribute traffic
- ✓ B.An inference script (e.g., score.py) to handle prediction requests
- ✓ C.A model artifact containing the model files
- D.An API signing key for authentication
- ✓ E.A deployment configuration specifying resources and environment
Why B: Option B is correct because OCI Data Science model deployment requires an inference script (typically score.py) to define how the model processes incoming prediction requests. This script is the entry point that loads the model artifact and executes inference logic, making it an essential component for serving predictions.
Variation 2. A developer wants to deploy a custom generative AI model that was trained using OCI Data Science. Which service should they use to expose the model as an API endpoint?
easy- A.OCI API Gateway
- ✓ B.OCI Data Science Model Deployment
- C.OCI Functions
- D.OCI Generative AI service
Why B: B is correct because OCI Data Science Model Deployment is specifically designed to host and serve machine learning models as REST API endpoints. It directly deploys models trained in OCI Data Science, managing the underlying infrastructure, scaling, and providing a secure HTTPS endpoint for inference requests.
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Last reviewed: Jun 24, 2026
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