Exam Certified Data Engineer Associate topic 1 question 36 discussion

D. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics. To identify the table in a Delta Live Tables (DLT) pipeline where data is being dropped due to quality concerns, the data engineer can navigate to the DLT pipeline page, click on each table in the pipeline, and view the data quality statistics. These statistics often include information about records dropped, violations of expectations, and other data quality metrics. By examining the data quality statistics for each table in the pipeline, the data engineer can determine at which table the data is being dropped.

The correct answer is: D. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics. Delta Live Tables provides detailed data quality statistics for each table in the pipeline. By navigating to the DLT pipeline page and clicking on each table, the data engineer can view these statistics and determine at which table the records are being dropped due to quality concerns. This allows them to identify and address the specific issues causing the data to be dropped.

Correct answer is "D".

I would say D but I have never really tested it, still other solutions smell wrong

D is correct By clicking on each table in the DLT pipeline page, the data engineer may be able to access data quality statistics, error logs, or other information related to dropped records. This can help them pinpoint at which table in the pipeline the data is being dropped.

E is for when an error occur. But pipeline is defined to drop some records that will not result on error

D is correct

E When records are dropped due to quality concerns in a DLT pipeline, the errors are logged in the event log. The data engineer can navigate to the DLT pipeline page and click on the “Error” button to view the present errors. The errors will show the table where the records were dropped. Option A: Setting up separate expectations for each table will not help the data engineer determine which table is dropping the records. Option B: The data engineer cannot determine which table is dropping the records without looking at the event log. Option C: Setting up DLT to notify the data engineer via email when records are dropped will not help the data engineer determine which table is dropping the records. Option D: Viewing the data quality statistics for each table will not help the data engineer determine which table is dropping the records.

Think answer is D. The pipeline is configured to drop invalid records, i.e. a SQL equivalent query with a ON VIOLATION DROP ROW clause. This will not result in a failed pipeline execution because there are no errors. Instead, you'd have to go to each table and review the quality charactistics.

Is this for v2 or v3

The correct answer is D