Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

The best option to meet the ACID compliance and minimal human intervention requirements is to configure a Cloud SQL for PostgreSQL instance with high availability enabled. Key reasons: Cloud SQL for PostgreSQL provides full ACID compliance, unlike Bigtable which provides only atomicity and consistency guarantees. Enabling high availability removes the need for manual failover as Cloud SQL will automatically failover to a standby replica if the leader instance goes down. Point-in-time recovery in MySQL requires manual intervention to restore data if needed. BigQuery does not provide transactional guarantees required for an ACID database. Therefore, a Cloud SQL for PostgreSQL instance with high availability meets the ACID and minimal intervention requirements best. The automatic failover will ensure availability and uptime without administrative effort.

 If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK, because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and also control the level of access and the duration of the sharing. References:

Customer-managed Cloud KMS keys

[Authorized views]

[Analytics Hub overview]

[Creating an Analytics Hub listing]

A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired. Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. References:

Materialized views, ML models in data warehouse - Google Cloud

Data Engineering with Google Cloud Platform - Packt Subscription

To improve the performance of visualization queries while minimizing maintenance overhead, using materialized views is the most effective solution. Here’s why option C is the best choice:

Materialized Views:

Materialized views store the results of a query physically, allowing for faster access compared to regular views which execute the query each time it is accessed.

They can be automatically refreshed to reflect changes in the underlying data.

Incremental Updates:

The incremental updates capability of BigQuery materialized views ensures that only the changed data is processed during refresh operations, significantly improving performance and reducing computation costs.

This feature helps maintain up-to-date data in the materialized view with minimal processing time, which is crucial for data that needs to be no less than 4 hours old.

Performance and Maintenance:

By using materialized views, you can pre-compute and store the results of complex queries involving outer joins and analytic functions, resulting in faster query performance for data visualizations.

This approach also reduces the maintenance overhead, as BigQuery handles the incremental updates and refreshes automatically.

Steps to Implement:

Create Materialized Views:

Define materialized views for the visualization queries with the necessary configurations

CREATE MATERIALIZED VIEW project.dataset.view_name

AS

SELECT ...

FROM ...

WHERE ...

Enable Incremental Updates:

Ensure that the materialized views are set up to handle incremental updates automatically.

Reference in Visualization Tool:

Update the data visualization tool to reference the materialized views instead of running the original queries directly.

Reference Links:

BigQuery Materialized Views

Optimizing Query Performance