20+ practice questions focused on Data Acquisition and Preparation — one of the most tested topics on the CompTIA Data+ (DA0-002) exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Data Acquisition and Preparation PracticeA data analyst needs to extract data from an API that returns JSON. The analyst wants to convert the JSON output into a tabular format for analysis. Which function in a scripting language is commonly used for this purpose?
Explanation: `json_normalize()` is a function in the pandas library specifically designed to flatten semi-structured JSON data (including nested lists and dictionaries) into a tabular DataFrame. This makes it the ideal tool for converting API responses with complex nesting into rows and columns for analysis, unlike simpler JSON parsing functions.
A data analyst sees this error in the ETL logs. What is the most likely cause?
Explanation: The error indicates that the materialized view's underlying data has changed since its last refresh. Materialized views rely on change tracking (e.g., logs or timestamps) to perform incremental refreshes. If the source data was modified after the last refresh, the view cannot be incrementally refreshed—a full refresh is required instead. This mismatch between the view's snapshot and the source data is a common cause of refresh failures in ETL processes.
A data engineer is configuring access to a data lake in Amazon S3. What does the JSON policy shown allow?
Explanation: Based on the correct answer, the JSON policy shown implicitly grants the `s3:GetObject` action, which allows reading objects from the specified S3 bucket. In AWS IAM, reading objects requires explicit `GetObject` permission, making B the only correct choice.
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?
Explanation: 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.
A data analyst is merging two datasets from different departments. The analyst notices that the 'CustomerID' field in the first dataset is stored as an integer, while in the second dataset it is stored as a string with leading zeros. Which TWO steps should the analyst take to ensure successful data integration?
Explanation: Converting the integer CustomerID to a string ensures both datasets have a compatible data type for the join. This approach preserves the leading zeros in the second dataset, which are semantically significant (e.g., '00123' vs. 123). A left join is appropriate to retain all records from the primary dataset while matching on the converted key.
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Practice all Data Acquisition and Preparation questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Data Acquisition and Preparation. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Data Acquisition and Preparation questions on the DA0-002 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Data Acquisition and Preparation is tested as part of the CompTIA Data+ (DA0-002) blueprint. Practicing with targeted Data Acquisition and Preparation questions ensures you can handle any format or difficulty that appears.
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