Google Professional Data Engineer
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Ultimate access to all questions.
You are in the process of migrating your existing on-premises data into BigQuery on Google Cloud. Depending on the specific use case, you are considering either streaming or batch-loading methods for data transfer. It is also essential to mask some sensitive data before loading it into BigQuery to ensure data security and compliance. You require a cost-effective, programmatic solution to achieve these objectives. What steps should you take?
A
Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.
B
Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery, use the connection to the Cloud Data Loss Prevention (Cloud DLP) API to de-identify the necessary data.
C
Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming, batch processing, and Cloud DLP. Select BigQuery as your data sink.
D
Set up Datastream to replicate your on-premise data on BigQuery.