Three full-length practice tests for the Oracle Cloud Infrastructure Data Science Professional (1Z0-1110-26) certification exam - 50 questions per test, 90 minutes, 68% to pass, unlimited retakes.
Every question is grounded in Oracle's official exam objectives and product documentation - written fresh for this course, not recycled from public dumps.
What you'll be tested on
- OCI Data Science notebook sessions, projects, and model catalog
- Building, training, and evaluating machine learning models
- Model deployment, versioning, and MLOps pipelines on OCI
- Data labeling, feature engineering, and AutoML
- Integrating Data Science with other OCI services for end-to-end ML workflows
Where the exam bites
Early exam content covers Data Science service setup and configuration, but where candidates actually lose points is deeper in: reasoning about model deployment options (real-time endpoints versus batch scoring), how autoscaling and load balancing behave once a model is deployed, and how the service hands off to Data Flow and the Model Catalog for versioning. Candidates coming from a pure data-science background without much OCI operations experience also tend to underestimate the IAM and networking questions, since deploying a notebook session or model endpoint inside a private subnet requires understanding VCNs, network security groups, and dynamic groups - not just model training.
Sample question
A data scientist has registered a trained model in the OCI Data Science Model Catalog and wants to expose it as a REST endpoint that automatically scales the number of model deployment instances based on incoming request load. Which OCI Data Science feature should be configured to achieve this?
- A. A notebook session with a custom conda environment attached to the model's compartment.
- B. A model deployment configured with an autoscaling policy based on a defined metric threshold, such as CPU utilization.
- C. A Data Flow application scheduled to run predictions in batch mode on the model artifact.
- D. A Data Catalog harvest job pointed at the model artifact in Object Storage.
Full explanations and 149 more questions like this are in the practice tests.
How long to prepare
Because the exam leans on both ML fundamentals and OCI-specific deployment mechanics, 3-4 weeks of focused study is typical for someone with existing data-science or ML experience who is newer to OCI's Data Science service. If notebook sessions, model catalogs, and MLOps pipelines are all new to you as well, plan for 5-6 weeks instead.
