Three Required Components for OCI Data Science Model Deployment
Which THREE components are required to deploy a custom generative AI model on OCI Data Science model deployment?
Quick Answer
The answer is the inference script, deployment configuration, and model artifact. These three components are required for custom model deployment on OCI Data Science because the inference script—typically named score.py—serves as the entry point that loads the model artifact and executes the prediction logic, while the deployment configuration specifies the compute resources and environment settings needed to run that script. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of the minimal building blocks for serving a custom model, often appearing as a straightforward multiple-select item where the common trap is omitting the inference script in favor of optional components like autoscaling policies or logging. A reliable memory tip is to think of the three as “the brain (model artifact), the hands (inference script), and the stage (deployment configuration)”—without any one, the model cannot serve predictions.
⚠ Common exam trap
Test-takers frequently confuse optional infrastructure components like load balancers or API keys with the mandatory deployment components, leading them to select A or D instead of recognizing that the inference script, model artifact, and deployment configuration are the three required elements.
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
✓
An inference script (e.g., score.py) to handle prediction requests
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A load balancer to distribute traffic
Why it's wrong here
Load balancer is automatically provisioned if you enable it, but not required.
- ✓
An inference script (e.g., score.py) to handle prediction requests
Why this is correct
Required to define how the model is called.
- ✓
A model artifact containing the model files
Why this is correct
Required for the model to be deployed.
- ✗
An API signing key for authentication
Why it's wrong here
Authentication can be done via instance principals or other methods.
- ✓
A deployment configuration specifying resources and environment
Why this is correct
Defines compute shape, replicas, etc.
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Variation 1. Which THREE steps are required to deploy a custom generative AI model using OCI Data Science Model Deployment?
hard- A.Fine-tune the model using OCI Generative AI service
- ✓ B.Create a model artifact (e.g., pickle, ONNX) with inference code
- C.Register the model in OCI Generative AI service
- ✓ D.Upload the model artifact to an OCI Object Storage bucket
- ✓ E.Create a model deployment using the OCI Data Science Model Deployment service
Why B: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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