1Z0-184-25 Question 151
Select 2You have a manufacturing client that sends high-frequency sensor data from hundreds of IoT devices into Oracle Cloud Infrastructure (OCI). They want to detect equipment anomalies in near-real time and automatically notify operations staff. The data should also be stored for further analytics. Which actions should you implement using OCI� AI capabilities to meet these requirements?
- A
Implement Oracle Streaming to ingest sensor data, apply Oracle Anomaly Detection for real-time analysis, and use OCI Notifications to alert the operations team. Store flagged anomalies in Autonomous Database for historical analysis.
- B
Deploy Oracle Digital Assistant to ingest the sensor data directly and use built-in intents to classify anomalies before sending email notifications to operations staff.
- C
Use an OCI Data Science notebook to schedule a batch inference job that invokes a custom anomaly detection model. Store results in Autonomous Database and configure an Event Rule to trigger notifications.
- D
Stream data to Object Storage at regular intervals. Download the data for manual anomaly checks using a third-party tool, and then email a weekly report to the operations team.
- E
Enable Oracle Logging Analytics with a custom parser to interpret sensor data, filter errors, and archive the results in an on-premises database.
Show answer and explanation
Correct answers: A, C
Explanation
When designing an anomaly detection solution in OCI, you can use Oracle Streaming to handle real-time data ingestion, Oracle Anomaly Detection (part of AI Services) or a custom OCI Data Science model for anomaly scoring, and OCI Notifications for automated alerts. Storing the flagged anomalies in an Autonomous Database ensures that operations teams can run analytics or historical trend analyses. Refer to the OCI AI Services documentation for more details on configuring Oracle Anomaly Detection and OCI Data Science for streaming data scenarios.
- A. Correct.
Option 1 is correct. Oracle Streaming can capture the sensor data in real time. Oracle Anomaly Detection, which is part of OCI� AI Services, handles detecting anomalies on the fly. Storing detected anomalies in an Autonomous Database provides a scalable analytics repository, and OCI Notifications can automatically send alerts to operations teams.
- B. Incorrect.
Option 2 is incorrect. Oracle Digital Assistant is designed mainly for conversational interfaces, chatbots, and workflow automation. It does not natively provide real-time anomaly detection for sensor data, making it less optimal for this scenario.
- C. Correct.
Option 3 is correct. Running Oracle Data Science notebooks with a custom model on a schedule (e.g., hourly or more frequently) is a valid approach. Results can be stored in an Autonomous Database. Using OCI event rules or hooks to trigger Notifications after scoring is a common best practice for alerting teams to anomalies quickly.
- D. Incorrect.
Option 4 is incorrect. Streaming all data to Object Storage and manually analyzing it with a third-party tool fails the near-real-time requirement. The weekly email also delays critical issue alerts, risking equipment downtime or damage.
- E. Incorrect.
Option 5 is incorrect. Oracle Logging Analytics is more specialized for log data (e.g., system logs, application logs) rather than live sensor metrics. It does not directly integrate with Anomaly Detection for real-time equipment monitoring or automated notifications in this use case.