1Z0-1151-25 Question 46
Select 2Your organization hosts a MySQL Database Service instance with HeatWave on Oracle Cloud Infrastructure to run real-time analytics on e-commerce data. The analytics team reports that queries are suddenly running slower, and you discover that the transactional data is updated frequently but not consistently refreshed in the HeatWave cluster. Additionally, peak loads occur sporadically throughout the day. Which two actions should you take to ensure the analytics queries benefit from HeatWave while minimizing service disruptions?
- A
Manually flush the database cache on the MySQL DB system every time new transactional data is committed
- B
Regularly reload updated data into the HeatWave cluster after transactional updates
- C
Increase the HeatWave cluster size or node count to handle peak concurrency without performance degradation
- D
Create a separate Autonomous Database for analytics to offload all reporting queries
- E
Enable automatic patch updates on the MySQL Database Service to ensure the HeatWave cluster is always on the latest version
Show answer and explanation
Correct answers: B, C
Explanation
HeatWave accelerates MySQL queries by offloading analytics operations to an in-memory, columnar data store. However, the cluster must be updated with the latest data from the transactional tables and sized appropriately to handle peak workloads. According to Oracle� best practices, you should perform regular data loads or refreshes into HeatWave (Option 2) and scale the HeatWave cluster as needed (Option 3) to maintain optimal performance. Refer to the Oracle Cloud Infrastructure MySQL Database Service with HeatWave documentation for detailed guidelines on performing data refreshes and scaling the cluster.
- A. Incorrect.
Option 1: Manually flushing the database cache does not load data into HeatWave. The HeatWave layer requires explicit data loading to be updated, so simply flushing the cache will not ensure your analytics queries are using the fresh data. This option is incorrect.
- B. Correct.
Option 2: Reloading or updating data in the HeatWave cluster is essential every time the transactional tables change significantly. This ensures that analytics queries run on the latest snapshot of the data within HeatWave, improving query performance. This option is correct.
- C. Correct.
Option 3: Increasing the HeatWave cluster size or node count addresses concurrency issues and helps maintain performance during peak loads. By scaling out the cluster, your analytics queries can handle more parallel operations. This option is correct.
- D. Incorrect.
Option 4: While deploying a separate Autonomous Database for reporting is a valid approach for some use cases, moving all queries to Autonomous Database does not directly leverage your existing HeatWave setup for real-time analytics. It also introduces data movement overhead. This does not solve the core issue of properly refreshing and scaling your HeatWave environment. This option is incorrect.
- E. Incorrect.
Option 5: Enabling automatic patch updates can keep your system up to date, but it does not address the need to regularly refresh the HeatWave cluster with new data or handle concurrency demands. Patching alone will not resolve query performance issues caused by stale data or insufficient cluster size. This option is incorrect.