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Free PDF Quiz 2025 CompTIA DA0-001: CompTIA Data+ Certification Exam High Hit-Rate Unlimited Exam Practice

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CompTIA Data+ Certification Exam is an industry-standard certification that is recognized globally. It is designed to help IT professionals demonstrate their skills and knowledge in data management. CompTIA Data+ Certification Exam certification is ideal for those who work in data-driven industries such as finance, healthcare, and retail, as well as those who work in IT support and operations.

CompTIA Data+ Certification Exam Sample Questions (Q284-Q289):

NEW QUESTION # 284
A company's marketing department wants to do a promotional campaign next month. A data analyst on the team has been asked to perform customer segmentation, looking at how recently a customer bought product, at what frequency, and at what value. Which of the following types of analysis would this practice be considered?

A. Prescriptive
B. Trend
C. Custer
D. Gap

Answer: C

NEW QUESTION # 285
Given the table below:

Which of the following variables can be considered inconsistent, and how many distinct values should the variable have?

A. Name, one
B. Region, five
C. Gender, two
D. Code, four
E. Level, three

Answer: C

Explanation:
The table provided shows an inconsistency in the 'Gender' column, which lists three distinct values: Male, Female, and College. This is inconsistent because 'College' is not a gender category. The 'Gender' column should only have two distinct values, typically 'Male' and 'Female', to accurately represent gender data. This error could be due to a data entry mistake or a misclassification during data collection.
In data analysis, it's crucial to ensure that categorical variables like gender are consistent and correctly classified, as this can significantly impact the analysis results. Data cleaning processes often involve identifying and correcting such inconsistencies to maintain the integrity of the data set.
References:
* Data quality management principles emphasize the importance of consistency in data values, especially for categorical variables like gender1.
* Best practices in data cleaning include checking for and rectifying inconsistencies or misclassifications in data sets2.
* The importance of accurate data classification is highlighted in data analysis literature, as it directly affects the validity of the analysis results3.

NEW QUESTION # 286
Which of the following is a control measure for preventing a data breach?

A. Data transmission
B. Data retention
C. Data encryption
D. Data attribution

Answer: C

Explanation:
Explanation
This is because data encryption is a type of control measure that prevents a data breach, which is an unauthorized or illegal access or use of data by an external or internal party. Data encryption can prevent a data breach by protecting and securing the data using a code or a key that scrambles or transforms the data into an unreadable or incomprehensible format, which can only be decoded or restored by authorized users who have the correct code or key. For example, data encryption can prevent a data breach by encrypting the data in transit or at rest, such as when the data is sent over a network or stored in a device. The other control measures are not used for preventing a data breach. Here is why:
Data transmission is a type of process that transfers and exchanges data between different sources or systems, such as databases, cloud services, or web applications. Data transmission does not prevent a data breach, but rather exposes the data to potential risks or threats during the transfer or exchange.
However, data transmission can be made more secure and less vulnerable to a data breach by using encryption or other methods, such as authentication or authorization.
Data attribution is a type of feature or function that assigns and tracks the ownership and origin of the data, such as the creator, modifier, or source of the data. Data attribution does not prevent a data breach but rather provides information and evidence about the data provenance and history. However, data attribution can be useful for detecting and responding to a data breach by using audit logs or metadata to identify and trace any unauthorized or illegal access or use of the data.
Data retention is a type of policy or standard that specifies and regulates the storage and preservation of the data, such as the duration, location, or format of the data. Data retention does not prevent a data breach, but rather affects the availability and accessibility of the data for future use or reference.
However, data retention can be optimized and aligned with the legal and ethical requirements and standards of the industry or the organization to reduce the risk or impact of a data breach.

NEW QUESTION # 287
Which of the following data elements would not normally be stored in binary format?

A. Video recording.
B. Geolocation.
C. Audio recording.
D. Photograph.

Answer: B

NEW QUESTION # 288
Given the following data:
CustomerID
ItemBought
Date
Tre_234
Sofa
2022-09-08
216_Tre
Shoes
08/02/2021
215/Tre
Blanket
2021/06/20
045/Tre
Mug
12-26-2021
Tre-345
Lamp
31/08/2022
TREJD19
Bucket
2022'08/01
Which of the following best describes the main issue in the data set?

A. Data mismatch
B. Invalid data
C. Inconsistent data
D. Redundant data

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The dataset exhibits inconsistencies in both the CustomerID and Date columns:
* CustomerID Column: The identifiers use various formats, including underscores (Tre_234), slashes (215
/Tre), hyphens (Tre-345), and combinations of letters and numbers (TREJD19). This inconsistency can lead to difficulties in data retrieval and analysis.
* Date Column: Dates are presented in multiple formats, such as YYYY-MM-DD (2022-09-08), MM/DD
/YYYY (08/02/2021), YYYY/MM/DD (2021/06/20), MM-DD-YYYY (12-26-2021), DD/MM/YYYY (31/08/2022), and an unconventional format (2022'08/01). Such variability can cause errors in date computations and analyses.
Addressing these inconsistencies is essential to ensure data quality and reliability.

NEW QUESTION # 289
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