Exam Associate Data Practitioner topic 1 question 6 discussion

A isnt right as Cloud Run isn't well suited to Data pipelines and generally isn't recommended. It *can* handle processing data, but not large volumes of batch data and isn't intended as an ETL tool. C isn't right because Ingesting first into BigQuery doesn't meet the requirement to cleansing BEFORE ingestion. D isn't right because Apache BEAM isn't a managed solution (although if the data had to be cleansed before leaving the on prem network it might be a suitable option to install on prem) Therefore B (Data Fusion) is the best opinion. If DataFlow was here, then that would also be a good choice - perhaps a better one - but without it, Fusion is the obvious winner.

The best option is B. Cloud Data Fusion. Option B is best because Data Fusion is a managed, visual, standardized data integration service ideal for building de-identification pipelines. Option A (Cloud Run functions) is incorrect because it requires more coding and is less inherently standardized for pipelines. Option C (Load to BigQuery first) is incorrect because it violates the requirement to de-identify before ingestion, creating a security risk. Option D (Apache Beam/Dataflow) is incorrect because while powerful, it's more code-centric and less of a pre-built managed solution compared to Data Fusion. Therefore, Option B, Cloud Data Fusion, is the best managed and standardized solution for pre-ingestion PII de-identification.

Cloud Data Fusion can be used to Sensitive Protection Service to de-identify feeds. However, Cloud Dataflow (a Google managed version of Beam) is the more general approach being used. I am only selecting Data Fusion over Beam because it is a named Google service. Had they said Dataflow, I would have gone there instead.