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.
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.
Which THREE components are required to deploy a custom generative AI model on OCI Data Science model deployment?
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
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.
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.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
A model artifact containing the model files
Why this is correct
Required for the model to be deployed.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
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 that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, OCI Data Science model deployment creates a managed HTTP endpoint that invokes the inference script (e.g., score.py) for each request, passing input data as JSON. The model artifact must be a zip file containing the serialized model (e.g., PyTorch .pt or TensorFlow SavedModel) and any dependencies, while the deployment configuration specifies CPU/memory, environment variables, and scaling policies. A real-world scenario: if you omit the inference script, the deployment fails because the service has no entry point to load the model and handle requests.
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: An inference script (e.g., score.py) to handle prediction requests — 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.
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
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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 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: 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.
Last reviewed: Jun 24, 2026
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