Explanation: Pipelines accept structured input formats such as JSON or CSV, allowing data scientists to parameterize pipeline runs. Inputs are used to control behavior like data location or hyperparameters. What happens if a step fails during pipeline execution?
Explanation: OCI Pipelines can be triggered programmatically through cron jobs or automatically via OCI Functions and Events, enabling fully automated MLOps pipelines. Which OCI Data Science object can be reused across pipelines to manage dependencies and environments?
Objective: Identify which activity isn’t part of the ML lifecycle. Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring. Evaluate Options: A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows. B: Model Deployment (e.g., serving predictions) is a key ML phase —correctly included. C: Modeling (e.g., training) is the core of ML —correctly included. D: Data Access (e.g., retrieving data) is the first ML step—correctly included. Reasoning: Database management supports infrastructure, not the ML process directly. Conclusion: A is the outlier. The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML -specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline. : Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview". Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
Which step is a part of the AutoML pipeline?
action taken after an event/rule —fits
Detailed Answer in Step -by-Step Solution: Objective: Find a true statement about ML models. Evaluate Options: A: True —Data drift (changes in data distribution) degrades performance over time. B: False —Static predictions don’t improve without retraining. C: False —Models need updates as data changes, unlike static software. D: False —Even high -quality models require retraining with new data. Reasoning: A reflects the reality of data drift, a common ML challenge. Conclusion: A is correct. OCI documentation notes: “Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining.” B, C, and D contradict this—static predictions don’t improve (B), models aren’t static (C), and retraining is needed (D). A is the accurate aspect. : Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift". Which statement about logs for Oracle Cloud Infrastructure Jobs is true?
