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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 Apr. 15, 2026 (see below)
Which of the following would be considered non-personally identifiable information?
Non-personally identifiable information (non-PII) is any data that cannot be used to identify, contact, or locate a specific individual, either alone or combined with other sources. Non-PII can include aggregated statistics, anonymous data, device identifiers, IP addresses, cookies, and other types of information that do not reveal the identity or location of a person. Cell phone device name is an example of non-PII, as it does not reveal any personal information about the owner or user of the device. Therefore, the correct answer is A. Reference: What is Non-Personally Identifiable Information (Non-PII)? | Definition and Examples, What is Personally Identifiable Information (PII)? | Definition and Examples
A data engineer is creating a database field to capture whether a customer likes vanilla ice cream. Which of the following data types is the best to capture this information?
Comprehensive and Detailed In-Depth
When designing a database field to capture a binary preference, such as whether a customer likes vanilla ice cream, the most appropriate data type is:
Option B:Boolean
Rationale:A Boolean data type is used to represent binary values, typically TRUE or FALSE. In this context, it efficiently captures whether a customer likes (TRUE) or does not like (FALSE) vanilla ice cream.
Option A:Integer
Rationale:While integers represent whole numbers, using them to denote binary choices (e.g., 1 for 'likes' and 0 for 'dislikes') is less intuitive and can lead to ambiguity without proper context.
Option C:Categorical
Rationale:Categorical data types are used for fields that can take on one of a limited set of values, representing different categories. While 'likes' and 'dislikes' could be categories, a Boolean is more efficient for binary choices.
Option D:Numeric
Rationale:Numeric data types encompass both integers and floating-point numbers. Using a numeric type for a binary preference is unnecessary and could lead to data integrity issues.
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You are working with a dataset and need to swap the values in rows with those in columns.
What action do you need to perform?
Transpose creates a new data file in which the rows and columns in the original data file are transposed so that cases (rows) become variables and variables (columns) become cases. Transpose automatically creates new variable names and displays a list of the new variable names.
Transposing data is useful for data analysis. At times, we have to pull data from various files with different formats for analysis and preparing reports. In such circumstances, we may have to transpose some data from one file to the other. In excel, we can transpose data in multiple ways.
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
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