DA0-002 Data Acquisition and Preparation Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Merge all records without deduplication

  Why it's wrong here

  Merging without deduplication cannot resolve the absent common identifier, so the same patient appears as multiple unlinked records. Probabilistic matching, comparing attributes such as name, date of birth and postcode, is required. Merging suits datasets already sharing a reliable unique key, where consolidation alone is sufficient.

• ✗

  Generate random unique IDs for each system

  Why it's wrong here

  Generating random unique IDs per system creates disjoint keys, so records for the same patient across hospitals cannot be matched. The technique suits assigning surrogate keys within a single system, not reconciling records lacking a shared identifier. Probabilistic matching, comparing attributes such as name and date of birth, is required here.

• ✗

  Manually match records for all patients

  Why it's wrong here

  Manual matching cannot scale across multiple hospitals' full patient populations and introduces inconsistent human judgement. Probabilistic or deterministic record linkage using demographic fields is required. Manual review would be correct only for a small candidate set flagged by an automated matching pass.

• ✓

  Probabilistic record linkage using name, DOB, and ZIP

  Why this is correct

  Without a common identifier, deterministic joins fail, so probabilistic record linkage compares name, date of birth and ZIP to compute match weights across hospitals. This satisfies the stem's constraint of linking records lacking a shared key, tolerating minor discrepancies between systems.