Google Updated Professional-Data-Engineer Exam Questions and Answers by stevie

To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here’s why option C is the best choice:

Analytics Hub:

Analytics Huballows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.

This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.

Data Publishing and Subscribing:

Teams can publish datasets they control, allowing them to manage access and updates independently.

Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.

Minimized Operational Tasks and Costs:

This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.

By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.

Steps to Implement:

Set Up Analytics Hub:

Enable Analytics Hub in your Google Cloud project.

Provide training to teams on how to publish and subscribe to data exchanges.

Publish Data:

Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.

Subscribe to Data:

Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.

Reference Links:

Analytics Hub Documentation

Publishing Data in Analytics Hub

Subscribing to Data in Analytics Hub

To optimize the performance of a complex Spark job on Dataproc that heavily relies on shuffling operations, and given the cost constraints of using preemptible VMs, switching from HDDs to SSDs and using HDFS as an intermediate storage layer can significantly improve performance. Here’s why option C is the best choice:

Performance of SSDs:

SSDs provide much faster read and write speeds compared to HDDs, which is crucial for performance-intensive operations like shuffling in Spark jobs.

Using SSDs can reduce I/O bottlenecks during the shuffle phase of your Spark job, improving overall job performance.

Intermediate Storage with HDFS:

Copying data from Google Cloud Storage (GCS) to HDFS for intermediate storage can reduce latency compared to reading directly from GCS.

HDFS provides better locality and faster data access within the Dataproc cluster, which can significantly improve the efficiency of shuffling and other I/O operations.

Cost Considerations:

Although SSDs are more expensive than HDDs, the performance improvement for shuffle-heavy workloads can justify the cost, especially if the improved performance reduces the overall runtime and thereby the cost of using preemptible VMs.

Using preemptible VMs with SSDs for this workload balances the cost and performance trade-offs effectively.