Ensuring Compliance DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   Which masking function would you use to replace a PII value with a random but consistent substitute that can be reversed if needed?

   • ASHA-256 hashing
   • BTokenization
   • CBase64 encoding
   • DRedaction with '***'
   Reveal Solution Discussion Select an answer first
2. 2expert · hard

   A company must comply with GDPR, which requires that personal data be deleted upon request. They have a batch pipeline that processes user data and stores it in a Delta table. The pipeline must be designed to handle both masking for analytics and purging for deletion requests. Which design best meets these requirements?

   • AUse a single pipeline that only purges records, without masking, since deletion is the primary requirement.
   • BMask PII in the pipeline and store the original data in a separate table for deletion requests.
   • CUse a single pipeline that masks PII and then immediately deletes the masked records.
   • DCreate two separate pipelines: one that masks PII for analytics, and another that purges records based on a deletion request table.
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   A data engineer needs to identify which columns in a streaming dataset contain PII. Which approach is most appropriate for this task?

   • ARunning data profiling to detect patterns and distributions that suggest PII, such as email formats or high-cardinality identifiers
   • BAsking the data source owner which columns are considered PII
   • CReviewing the schema metadata to find columns with names like 'email' or 'ssn'
   • DUsing a machine learning model to classify each row as containing PII or not
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   Which Spark DataFrame operation is used to purge records that meet a retention rule?

   • Adf.filter(retention_rule).write.mode('overwrite').save(path)
   • Bdf.groupBy('id').count()
   • Cdf.select('non_pii_columns')
   • Ddf.dropDuplicates()
   Reveal Solution Discussion Select an answer first
6. 5foundation · easy

   In a streaming pipeline, which approach is used to purge records that exceed a retention period?

   • AUsing groupBy to aggregate old records
   • BStopping the stream and manually deleting files
   • CUsing a watermark and a filter to drop old records
   • DUsing a UDF to delete records from the source
   Reveal Solution Discussion Select an answer first