CompTIA A+ DA0-001 Practice Test

DA0-001 practice test: what's covered

• Data Concepts
• Mining
• Analysis
• Visualization
• Governance

Free DA0-001 sample questions

1. An analyst is profiling a customer table and finds a column storing values like 'Active', 'Inactive' and 'Pending'. Which data type best classifies this field?

   • A. Discrete numeric value
   • B. Categorical (nominal) data
   • C. Continuous numeric metric
   • D. Ordinal interval scale
   Show answer

   Correct: B. Categorical (nominal) data classifies values into distinct groups without any intrinsic order or ranking. Since 'Active', 'Inactive', and 'Pending' represent unranked categories, nominal classification is correct. Discrete and continuous options represent numeric metrics, while ordinal implies a ranked order.

2. A retail company needs a system that records thousands of individual point-of-sale transactions per minute with fast inserts and updates. Which environment is designed for this workload?

   • A. Online Analytical Processing (OLAP)
   • B. Online Transaction Processing (OLTP)
   • C. Centralized Data Lake
   • D. Departmental Data Mart
   Show answer

   Correct: B. Online Transaction Processing (OLTP) environments are specifically architected for high-frequency, low-latency transaction processing such as point-of-sale inserts and updates. OLAP systems focus on historical analytical reporting, data lakes store raw unstructured/semi-structured files, and data marts are subject-specific analytical repositories.

3. A cloud data architect is comparing storage architectures for an enterprise analytics project. Which statement best describes a data lake compared to a traditional data warehouse?

   • A. A data lake stores only pre-modeled relational tables
   • B. A data lake stores raw structured, semi-structured, and unstructured data in its native format
   • C. A data lake enforces strict schema-on-write prior to data loading
   • D. A data lake cannot integrate with real-time streaming engines
   Show answer

   Correct: B. Data lakes store raw data across structured, semi-structured, and unstructured formats using schema-on-read principles. Data warehouses enforce strict schema-on-write models with cleansed data. The options asserting pre-modeled tables, schema-on-write, or lack of streaming support are incorrect characteristics of data lakes.

4. A finance department wants its own subject-specific subset of the enterprise data warehouse containing only finance data. What is this structure called?

   • A. Subject-specific Data Mart
   • B. Centralized Data Lake
   • C. Operational Data Store
   • D. ETL Staging Repository
   Show answer

   Correct: A. A Data Mart is a focused, departmental subset of an enterprise data warehouse tailored for specific business teams like finance. Data lakes contain enterprise-wide raw data, operational data stores serve real-time operational querying, and staging areas hold temporary data during ETL execution.

5. A pipeline loads raw data into a cloud data lake first and performs transformations afterward using the target platform's compute. Which approach is this?

   • A. Extract, Transform, Load (ETL)
   • B. Extract, Load, Transform (ELT)
   • C. Change Data Capture (CDC)
   • D. Distributed Data Federation
   Show answer

   Correct: B. Extract, Load, Transform (ELT) loads raw data directly into the target cloud repository first and executes transformations using the target engine's scalable compute power. Traditional ETL transforms data before loading, while CDC tracks row changes, and federation queries distributed sources virtually.

Last updated: 2026-10-19

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