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?
Explanation: Detailed Answer in Step -by-Step Solution: Objective: Identify a true statement about OCI Data Science Jobs. Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure. Evaluate Options: A: True —Jobs provision OCI compute resources on -demand for task execution. B: False —Users define custom tasks (e.g., Python scripts), not limited to standard ones. C: False —Infrastructure is fully managed by OCI, not user -managed. D: False —Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file. Reasoning: A reflects OCI’s managed, on -demand provisioning model for Jobs. Conclusion: A is correct. The OCI Data Science documentation states: “Jobs provision compute infrastructure on -demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources.” B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility —only A accurately describes Jobs. : Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview". Which step is unique to MLOps, as opposed to DevOps?
➢ 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?
