Exam AWS Certified DevOps Engineer - Professional DOP-C02 topic 1 question 195 discussion

While E suggests configuring the memory store retention period to be longer than the magnetic store retention period, this is typically not aimed at optimizing query performance but rather at keeping data in the faster-access memory store for longer periods, which could be beneficial for workloads requiring frequent access to recent data. However, for the scenario described, focusing on efficient data ingestion methods (A and D) and understanding the role of retention periods (F) provides a balanced approach to achieving the fastest query performance for daily queries.

E fast performance A writer more throughput D multi measure means less records to store each data point. Faster query

My only hesitation is in regards to how batch writes might improve query performance, other than if the stored data is in a contiguous chunk, that could hep a query later. As far as for multi-measure and more memory, I defer to references: A: (YES) "When writing data to InfluxDB, write data in batches to minimize the network overhead related to every write request." D: (YES) "Multi-measure records results in lower query latency for most query types when compared to single-measure records." E: (YES) "The memory store is optimized for high throughput data writes and fast point-in-time queries." F: (NO) "The magnetic store is optimized for lower throughput late-arriving data writes, long term data storage, and fast analytical queries."

ADF A - improve write performance and efficiency D - query for a specific measure in a multi-measure record, Timestream only scans the relevant measure, not the entire record. This means that even though the record contains multiple measures, the query performance for a specific measure is not negatively impacted. Multi-measure record reduces the number of records that need to be written and subsequently queried, which improve query performance. F - memory store, which is optimised for write and query performance, is not filled with older data that is not frequently accessed

ADF seems more relevant

ADF – batch writes, Treat log as multi-measure record, Memory story should be shorter,. https://aws.amazon.com/blogs/database/improve-query-performance-and-reduce-cost-using-scheduled-queries-in-amazon-timestream/#:~:text=Improve%20query%20performance%20and%20reduce%20cost%20using%20scheduled,6%20Query%20performance%20metrics%20...%207%20Conclusion%20

A. Batch writes: This significantly reduces overhead associated with individual write operations and improves overall write throughput. C. Single-measure record: For daily queries summarizing multiple metrics, treating each log as a single record helps Timestream leverage its optimized storage and query processing for single measures. D. Multi-measure record: While it seems counterintuitive, Timestream performs better with multiple measures within a single record compared to separate records for each metric. This allows for efficient data retrieval and aggregation during queries.

A,D and F are correct: A: do job in batch optimize costs and performance B: should not do single write C: The app emits multiple records the same time. So it should be multi-measure record, not single one D: correct E: Should not do this F: correct

ACE. Batch the write, write as whole and stay in memory longer