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DA0-001 Latest Exam Discount, Latest DA0-001 Test Report

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Introduction of CompTIA DA0-001 Exam

The CompTIA Data+ certification is a vendor neutral certification that validates the ability to use data to solve business problems. It is designed for professionals who work with or support IT, including IT manager, analyst, consultant and project manager.

CompTIA Data+ is focused on the skills necessary to analyze, manage and implement information technology systems within an organization. The exam covers how to collect and analyze data using tools such as spreadsheets and databases; how to use this data to make informed decisions about IT projects; how to create reports from the data; how to communicate findings clearly and concisely through presentations and reports; how to identify security issues related to information assets; and how to apply appropriate standards for data management practices across your company or organization. CompTIA DA0-001 Exam Dumps are the best practice exams.

The CompTIA Data+ certification is designed for professionals working in a variety of fields such as finance, marketing, human resources, sales and legal who use data in their jobs. The manipulation of the schedule programming dimensions driven updates role components activity interface busy campaigns advertising reasons activities with decision news.

One of the key benefits of obtaining the CompTIA Data+ Certification is the opportunity to advance your career in the data management field. CompTIA Data+ Certification Exam certification is recognized by many employers and provides a competitive advantage over other candidates who do not hold this certification. Additionally, certified professionals can expect to earn higher salaries than non-certified professionals in similar roles.

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CompTIA Data+ Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
An analyst has received the requirements for an internal user dashboard. The analyst confirms the data sources and then creates a wireframe. Which of the following is the NEXT step the analyst should take in the dashboard creation process?

A. Get stakeholder approval.
B. Deploy to production.
C. Optimize the dashboard.
D. Create subscriptions.

Answer: A

NEW QUESTION # 73
Given the table below:

Which of the following variable types BEST describes the "Year" column?

A. Alphanumeric
B. Text
C. Numeric
D. Date

Answer: D

Explanation:
This is because date is a type of variable that represents a specific point or period in time, such as a day, a month, or a year. Date variables can be used to store, manipulate, or analyze temporal data, such as transaction dates, birth dates, or expiration dates. For example, date variables can be used to calculate the duration or the difference between two dates, or to filter or sort the data by date. The other variable types are not correct descriptions of the "Year" column. Here is why:
* Numeric is a type of variable that represents a numerical value, such as an integer, a decimal, or a fraction. Numeric variables can be used to store, manipulate, or analyze quantitative data, such as amounts, prices, or scores. For example, numeric variables can be used to perform arithmetic operations or calculations on the data, or to measure the central tendency or the dispersion of the data.
* Alphanumeric is a type of variable that represents a combination of alphabetic and numeric characters, such as letters, numbers, symbols, or spaces. Alphanumeric variables can be used to store, manipulate, or analyze textual data, such as names, addresses, or codes. For example, alphanumeric variables can be used to concatenate or split the data, or to search or match the data using patterns or expressions.
* Text is a type of variable that represents a sequence of alphabetic characters, such as letters or words.
Text variables can be used to store, manipulate, or analyze textual data, such as names, categories, or labels. For example, text variables can be used to change the case or the length of the data, or to compare or classify the data using criteria or rules.

NEW QUESTION # 74
How many variables may be shown in a simple pie chart?

A. 0
B. 1
C. 2
D. 3

Answer: B

NEW QUESTION # 75
An analyst has been asked to validate data quality. Which of the following are the BEST reasons to validate data for quality control purposes? (Choose two.)

A. Deletion
B. Retention
C. Consistency
D. Integrity
E. Transmission
F. Encryption

Answer: D

Explanation:
Explanation
Integrity and D. Consistency. This is because integrity and consistency are two of the best reasons to validate data for quality control purposes, which means to check and ensure that the data is accurate, complete, reliable, and usable for the intended analysis or purpose. By validating data for integrity and consistency, the analyst can prevent or correct any errors or issues in the data that could affect the validity or reliability of the analysis or the results. Here is what integrity and consistency mean in terms of data quality:
Integrity refers to the completeness and validity of the data, which means that the data has no missing, incomplete, or invalid values that could compromise its meaning or usefulness. For example, validating data for integrity could involve checking for null values, outliers, or incorrect data types in the data set.
Consistency refers to the uniformity and standardization of the data, which means that the data follows a common format, structure, or rule across different sources or systems. For example, validating data for consistency could involve checking for spelling, punctuation, or capitalization errors in the data set.
The other reasons are not the best reasons to validate data for quality control purposes. Here is why:
Retention refers to the storage and preservation of the data, which means that the data is kept and maintained in a secure and accessible way for future use or reference. Retention does not need to be validated for quality control purposes, because it does not affect the accuracy or reliability of the data itself.
Transmission refers to the transfer and exchange of the data, which means that the data is moved or shared between different sources or systems in a fast and efficient way. Transmission does not need to be validated for quality control purposes, because it does not affect the completeness or validity of the data itself.
Encryption refers to the protection and security of the data, which means that the data is encoded or scrambled in a way that prevents unauthorized access or use. Encryption does not need to be validated for quality control purposes, because it does not affect the uniformity or standardization of the data itself.
Deletion refers to the removal and disposal of the data, which means that the data is erased or destroyed in a way that prevents recovery or retrieval. Deletion does not need to be validated for quality control purposes, because it does not affect the meaning or usefulness of the data itself.

NEW QUESTION # 76
Which one of the following is not a good example of discrete data type?

A. Temperature of a room.
B. Years of experience.
C. Attendees at a meeting.
D. Number of children.

Answer: A

NEW QUESTION # 77
......

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