patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?
Explanation: Detailed Answer in Step -by-Step Solution: Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions. Key Characteristics: Jobs run on OCI’s infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps. Evaluate Options: A: Correct —Jobs are defined by users (e.g., via scripts) and executed on OCI’s fully managed compute resources. B: Incorrect —Infrastructure is managed by OCI, not the customer. C: Incorrect —No third -party cloud integration; it’s OCI -specific. D: Incorrect —Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.g., CI/CD pipelines). Reasoning: “Fully managed” means OCI handles provisioning and scaling, aligning with
Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message"). Step 5: Push to remote: Push local commits to the remote repo (git push origin main). Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical. The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: “First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code.” D is the only option reflecting this industry -standard process. : Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics". While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?
What is feature engineering in machine learning used for?
D . Model catalog: Stores models and artifacts, enabling sharing and loading into sessions. Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK). Conclusion: D is the correct tool. The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose. : Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage". Which statement about resource principals is true?
➢ TOTAL QUESTIONS: 308 A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
