Which setting in the dataflow detail allows for ingesting data that contains errors up to a 30% threshold?

Which setting in the dataflow detail allows for ingesting data that contains errors up to a 30% threshold?

• A. Full ingestion
• B. Partial ingestion
• C. Batch ingestion
• D. Incremental ingestion

Answer: (B)

Partial ingestion is the correct choice for this question because it is specifically designed to allow for the ingestion of data even when there are errors present, up to a defined threshold. In the context of the Adobe Real-Time Customer Data Platform (RTCDP), partial ingestion provides the flexibility to include records that may not be completely accurate, thus avoiding the complete rejection of the data set based on the presence of errors. The ability to ingest data with errors helps in maintaining data flow continuity, especially in scenarios where some data is better than none. By accepting records that fall within an error threshold, organizations can still benefit from the usable data while addressing inaccuracies at a later stage. In contrast, options such as full ingestion would typically reject any data set that contains errors, requiring all records to be error-free before acceptance. Batch ingestion relates to how data is processed in groups or collections rather than addressing error thresholds specifically. Incremental ingestion focuses on adding or updating data progressively and does not inherently refer to error handling in the ingestion process.

Partial ingestion is the correct choice for this question because it is specifically designed to allow for the ingestion of data even when there are errors present, up to a defined threshold. In the context of the Adobe Real-Time Customer Data Platform (RTCDP), partial ingestion provides the flexibility to include records that may not be completely accurate, thus avoiding the complete rejection of the data set based on the presence of errors.

The ability to ingest data with errors helps in maintaining data flow continuity, especially in scenarios where some data is better than none. By accepting records that fall within an error threshold, organizations can still benefit from the usable data while addressing inaccuracies at a later stage.

In contrast, options such as full ingestion would typically reject any data set that contains errors, requiring all records to be error-free before acceptance. Batch ingestion relates to how data is processed in groups or collections rather than addressing error thresholds specifically. Incremental ingestion focuses on adding or updating data progressively and does not inherently refer to error handling in the ingestion process.