OCI 1Z0-1110-25 glossary of data prep, notebooks, training, deployment, and lifecycle terms.
On this page
Use this glossary to clean up high-confusion OCI data-science terms before you go back into mixed sets. On this exam, terminology mistakes usually hide a lifecycle-stage or object-choice mistake.
High-value terms
Term
What it means here
Why it matters on the exam
Artifact
a stored output from a workflow such as a dataset snapshot, model, or notebook result
artifacts anchor repeatability and traceability
Dataset
the collection of records used for exploration, training, validation, or inference input
confusion here often causes leakage or split mistakes
Deployment
the stage where a trained model is exposed to serve predictions
deployment is a lifecycle stage, not just a stored object
Experiment
a tracked run or workflow iteration used to compare behavior
strong answers separate exploration from repeatable delivery
Feature engineering
preparing and shaping raw data into useful model inputs
this belongs before reliable training and evaluation
Inference
applying a trained model to new data to generate predictions
training and inference are not interchangeable
Job run
a scheduled or triggered execution of a data or ML task
jobs usually signal repeatability, not ad hoc work
Model artifact
the saved trained model output that can later be evaluated or deployed
artifact existence alone does not prove serving readiness
Notebook session
an interactive workspace for exploration and iterative work
notebooks are useful, but they are not the whole platform strategy
Observability
the ability to monitor jobs, deployments, and supporting services
operations questions often hinge on this rather than on model theory
Common confusion pairs
Pair
Clean separation
Training vs inference
training builds or updates the model, inference uses the model on new inputs
Notebook work vs production workflow
notebook work is interactive and exploratory, production workflow is more repeatable and operationalized
Artifact vs dataset
an artifact is a saved output of work, a dataset is the underlying data used or produced
Model quality vs platform health
model quality is about prediction usefulness, platform health is about whether the workflow runs reliably
Batch processing vs deployed endpoint
batch processing handles grouped or scheduled work, a deployed endpoint responds to new requests
Validation split vs metric
the split defines what evidence you trust, the metric defines how you score it
Drift vs outage
drift is quality change over time, outage is service unavailability or failure
Fast recall anchors
If you see…
Think…
interactive exploration
notebook session
repeatable scheduled execution
job run
saved trained output
model artifact
live prediction path
deployment
If three terms blur together
Terms
Short reset
project, notebook, job
project organizes work, notebook is interactive work, job is repeatable execution
model artifact, deployment, inference
artifact is the stored model output, deployment exposes it, inference is the act of using it
evaluation, validation, metric
validation is how you test, metric is how you score, evaluation is the broader judgment
model quality, observability, rollback
quality measures usefulness, observability tells you what is happening, rollback is the recovery move