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CompTIA DA0-001 Exam Questions
- Topic 1: Data Concepts and Environments: This topic covers fundamental concepts related to data schemas, dimensions, and data types, along with a comparison of common data structures and file formats.
- Topic 2: Data Mining: In this topic, you'll learn about data acquisition, the importance of data cleansing and profiling, techniques for data manipulation and query optimization to prepare data for analysis.
- Topic 3: Data Analysis: Here, the focus is on applying descriptive statistical methods to analyze data in scenarios, understanding inferential statistical methods, and exploring different types of analysis techniques along with tools commonly used in data analytics.
- Topic 4: Visualization: This topic explores translating business requirements into report formats, designing effective reports and dashboards using appropriate components, methods for dashboard development, and selecting suitable visualization types.
- Topic 5: Data Governance, Quality, and Controls: It covers essential concepts of data governance, applying data quality control techniques in scenarios, and understanding master data management (MDM) principles.
Free CompTIA DA0-001 Exam Actual Questions
Note: Premium Questions for DA0-001 were last updated On Feb. 27, 2026 (see below)
Which option best concepts should be applied if a data set with 40 fields needs to be pared down to 20 fields and contains similar data across multiple fields?
Consolidation is the process of combining multiple elements into a single, more effective or coherent whole. In the context of data analytics, consolidation would involve merging similar fields to reduce the overall number of fields in a dataset. This is particularly useful when a dataset contains redundant or similar data across multiple fields, as it helps to simplify the data structure and improve efficiency. Techniques such as dimensionality reduction are often applied to achieve this, where the goal is to retain the most informative and representative features of the data while reducing the number of total features.
Applied Dimensionality Reduction --- 3 Techniques using Python1.
Seven Techniques for Data Dimensionality Reduction2.
Best practices when working with datasets3.
Effectively Handling Large Datasets4.
Which of the following contains alphanumeric values?
Alphanumeric values are values that contain both letters and numbers, such as A3J7. Theother options are numeric values, as they contain only numbers, such as 10.1E2, 13.6, and 1347. Reference:Guide to CompTIA Data+ and Practice Questions - Pass Your Cert
A table in a hospital database has a column for patient height in inches and a column for patient height in centimeters. This is an example of:
This is because redundant data is a type of data that is unnecessary or irrelevant for the analysis or purpose, which can affect the efficiency and performance of the analysis or process. Redundant data can be caused by having multiple data fields that store the same or similar information, such as patient height in inches and patient height in centimeters in this case. Redundant data can be eliminated or reduced by using data cleansing techniques, such as removing or merging the redundant data fields. The other types of data are not examples of data that is unnecessary or irrelevant for the analysis or purpose. Here is what they mean in terms of data quality:
Dependent data is a type of data that relies on or is influenced by another data field or value, such as a formula or a calculation that uses other data fields or values as inputs or outputs. Dependent data can be useful or important for the analysis or purpose, as it can provide additional information or insights based on the existing data.
Duplicate data is a type of data that is repeated or copied in a data set, which can affect the quality and validity of the analysis or process. Duplicate data can be caused by having multiple records or rows that have the same or similar values for one or more data fields or columns, such as customer ID or order ID. Duplicate data can be eliminated or reduced by using data cleansing techniques, such as removing or filtering out the duplicate records or rows.
Invalid data is a type of data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis or process. Invalid data can be caused by having values that do not match the expected format, type, range, or rule for a data field or column, such as an email address that does not have an @ symbol or a date that does not follow the YYYY-MM-DD format. Invalid data can be eliminated or reduced by using data cleansing techniques, such as validating or correcting the invalid values.
Which one of the following would not normally be considered a summary statistic?
Simply put, a z-score (also called a standard score) gives you an idea of how far from the mean a data point is. But more technically it's a measure of how many standard deviations below or above the population mean a raw score is. A z-score can be placed on a normal distribution curve.
Which one of the following in NOT a common data integration tool?
Cross-site Scripting (XSS) is a security vulnerability usually found in websites and/or web applications that accept user input.
XSS is a client-side vulnerability that targets other application users, while SQL injection is a server-side vulnerability that targets the application's database. How do I prevent XSS in PHP? Filter your inputs with a whitelist of allowed characters and use type hints or type casting.
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