Exam Professional Data Engineer topic 1 question 282 discussion

It should B, Storage Write API has "3 GB per second throughput in multi-regions; 300 MB per second in regions"

- BigQuery Storage Write API: This API is designed for high-throughput, low-latency writing of data into BigQuery. It also provides tools to prevent data duplication, which is essential for exactly-once delivery semantics. - Regional Table: Choosing a regional location for the BigQuery table could potentially provide better performance and lower latency, as it would be closer to the Dataflow job if they are in the same region.

- BigQuery Storage Write API: This API is designed for high-throughput, low-latency writing of data into BigQuery. It also provides tools to prevent data duplication, which is essential for exactly-once delivery semantics. - The multiregional table ensures that your data is highly available and can be streamed into BigQuery across multiple regions. It is better suited for high-throughput and low-latency workloads, as it provides distributed write capabilities that can handle large data volumes, such as the 1.5 GB per second you expect to stream.

https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery For new projects, we recommend using the BigQuery Storage Write API instead of the tabledata.insertAll method. The Storage Write API has lower pricing and more robust features, including exactly-once delivery semantics https://cloud.google.com/bigquery/docs/write-api#advantages

B is correct. When aiming for exactly-once delivery in a Dataflow streaming job, the key is to use the BigQuery Storage Write API, as it provides the capability to handle large-scale data ingestion with the correct semantics, including exactly-once delivery.

3 GB per second throughput in multi-regions; 300 MB per second in regions https://cloud.google.com/bigquery/quotas#write-api-limits

streamed into BigQuery with exactly-once delivery semantics >>> Storage Write API ingestion throughput into BigQuery to be about 1.5 GB per second >>> multiregional (check throughput rate here >>> https://cloud.google.com/bigquery/quotas#write-api-limits)

- Choosing a regional target BigQuery table ensures that data is stored redundantly in a single region, providing high availability and durability.

According to this documentation, its B https://cloud.google.com/bigquery/quotas#write-api-limits

Write API support 2.5 GB / sec speed and support exactly-once delivery semantics https://cloud.google.com/bigquery/docs/write-api#connections whereas in streaming duplicates can come and needed to remove them manually https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery#dataavailability

looking for this documentation https://cloud.google.com/bigquery/quotas#write-api-limits. 3 GB/s in multi-regions; 300MB/s in regions

To ensure that analysts do not see customer data older than 30 days while minimizing cost and overhead, the best option is: B. Use a timestamp range filter in the query to fetch the customer’s data for a specific range. This approach directly addresses the issue by filtering out data older than 30 days at query time, ensuring that only the relevant data is retrieved. It avoids the overhead and potential delays associated with garbage collection and manual deletion processes

option D

Option A

Voting on A