DA0-002 Data Acquisition and Preparation Practice Question
A healthcare organization is building a data warehouse to support population health analytics. The data sources include: (1) an electronic health record (EHR) system with a relational database containing patient demographics, diagnoses, and medications; (2) a claims system that generates CSV files daily; (3) patient-generated health data from mobile apps via a REST API returning JSON. The data engineer needs to design a data acquisition process that runs nightly. The EHR system has a change tracking mechanism that logs changes with timestamps. The claims CSV files are appended daily. The API supports filtering by date. The data warehouse uses a star schema with fact and dimension tables. The engineer must ensure data consistency and minimize load times. Which approach should the engineer take?
⚠ Common exam trap
Candidates often assume a staging area (Option C) is always required for data consistency, but the question specifically asks for the acquisition approach to minimize load times, and incremental extraction (Option B) directly achieves that without mandating a staging area.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Extract only new and changed EHR data using change tracking, extract the full claims CSV (since it's append-only), and extract API data filtered by the last extraction date
It uses incremental extraction for the EHR system (via change tracking) and the API (via date filtering), while performing a full extraction of the claims CSV since it is append-only and small enough to reload nightly. This minimizes load times by avoiding full re-extraction of large, slowly changing datasets, and ensures data consistency by capturing only new or modified records. The star schema in the data warehouse is then populated efficiently from these targeted extracts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform a full extraction of all data from all sources every night and load directly into the data warehouse
Why it's wrong here
Full extraction of the EHR database nightly is inefficient and time-consuming.
- ✓
Extract only new and changed EHR data using change tracking, extract the full claims CSV (since it's append-only), and extract API data filtered by the last extraction date
Why this is correct
This minimizes data transfer and load time while capturing all changes.
- ✗
Use a staging area to land all raw data first, then transform and load
Why it's wrong here
Staging is good but full extraction still occurs; incremental is better.
- ✗
Extract the EHR data using change tracking, extract the full claims CSV, and extract the API data using a full dump
Why it's wrong here
The claims CSV is append-only, so incremental is possible; API full dump is unnecessary.
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