ACTUAL COMPTIA DA0-001 EXAM QUESTIONS–SMART STRATEGY TO GET CERTIFIED

Actual CompTIA DA0-001 Exam Questions–Smart Strategy to Get Certified

Actual CompTIA DA0-001 Exam Questions–Smart Strategy to Get Certified

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CompTIA DA0-001 Certification Exam is a highly acclaimed certification exam designed for individuals interested in pursuing a career in data analytics. CompTIA Data+ Certification Exam certification is aimed at professionals who have a thorough understanding of data collection and analysis methods and can effectively analyze and present data insights to various stakeholders. CompTIA Data+ Certification Exam certification exam validates the ability to assess data requirements, analyze data sets, and create comprehensive reports based on the obtained data.

In order to pass the CompTIA DA0-001 Exam, individuals need to have a good understanding of data management and analysis concepts, as well as practical skills in manipulating, analyzing, and visualizing data. DA0-001 exam consists of 80 multiple-choice questions and has a time limit of 90 minutes. Passing the exam requires a score of 700 or higher, on a scale of 100-900. Overall, the CompTIA DA0-001 exam is an excellent certification for individuals who want to demonstrate their expertise in data management and analysis, and it can help them advance their careers in this field.

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As for the DA0-001 study materials themselves, they boost multiple functions to assist the learners to learn the DA0-001 learning dumps efficiently from different angles. For example, the function to stimulate the exam can help the exam candidates be familiar with the atmosphere and the pace of the Real DA0-001 Exam and avoid some unexpected problem occur such as the clients answer the questions in a slow speed and with a very anxious mood which is caused by the reason of lacking confidence.

CompTIA Data+ Certification Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Given the following report:

Which of the following components need to be added to ensure the report is point-in-time and static? (Select two).

  • A. A control group for the phrases
  • B. A summary of the KPIs
  • C. The time period lhe report covers
  • D. Filter buttons for the status
  • E. The date on which the report was run
  • F. The date when the report was last accessed

Answer: E,F

Explanation:
To ensure that a report is point-in-time and static, it should include the date when the report was last accessed and the date on which the report was run. These components confirm the specific time frame the data represents, making the report a fixed reference that does not change with subsequent data updates or accesses. This is crucial for accurate historical analysis and for maintaining the integrity of the data as it was at the time of the report's creation.
Reference:
Best practices in business reporting.
Importance of time-stamping in data analysis.
Guidelines for creating static reports in data analytics.


NEW QUESTION # 48
Which of the following data types best describe 4Ac1? (Select two).

  • A. Alphanumeric
  • B. String
  • C. Symbolic
  • D. Float
  • E. Numeric
  • F. Boolean

Answer: A,B

Explanation:
The term '4Ac1' is a combination of numbers and letters, which fits the definition of an alphanumeric string. Alphanumeric refers to a character set that contains both letters and numbers. In data analytics and programming, such a value is typically treated as a string, which is a sequence of characters. Strings can include letters, digits, and various other symbols.
A numeric data type would only include numbers, and a float is a specific kind of numeric data type that includes decimal points, neither of which applies to '4Ac1'. A boolean data type represents one of two values: true or false. Since '4Ac1' does not represent a true or false value, it cannot be classified as boolean. Lastly, symbolic is not a standard data type in the context of programming and data analytics.
Reference:
Understanding Python 3 data types1.
Basic Data Types in Python2.
Java Data Types3.


NEW QUESTION # 49
Which of the following variable name formats would be problematic if used in the majority of data software programs?

  • A. FirstName
  • B. First Name
  • C. First_Name_
  • D. First_Name

Answer: B

Explanation:
Explanation
This is because First Name is a variable name format that would be problematic if used in most of the data software programs, such as Excel, SQL, or Python. This is because First Name contains a space between two words, which could cause confusion or errors in the data software programs, as they might interpret the space as a separator or a delimiter between two different variables or values, rather than as part of a single variable name. For example, in SQL, a space is used to separate keywords, clauses, or expressions in a statement, such as SELECT, FROM, WHERE, etc. Therefore, using First Name as a variable name in SQL could result in a syntax error or an unexpected result. The other variable name formats would not be problematic if used in most of the data software programs. Here is why:
First_Name_ is a variable name format that uses an underscore (_) to separate two words, which is a common and acceptable practice in most of the data software programs, as it helps to improve the readability and clarity of the variable name. For example, in Python, an underscore is used to follow the PEP 8 style guide for naming variables, which recommends using lowercase letters and underscores for multi-word variable names.
FirstName is a variable name format that uses camel case to separate two words, which is another common and acceptable practice in most of the data software programs, as it helps to reduce the length and complexity of the variable name. For example, in Excel, camel case is used to follow the VBA naming conventions for naming variables, which recommends using mixed case letters for multi-word variable names.
First_Name is a variable name format that also uses an underscore (_) to separate two words, which is also a common and acceptable practice in most of the data software programs, as it helps to improve the readability and clarity of the variable name. For example, in SQL, an underscore is used to follow the ANSI SQL naming standards for naming variables, which recommends using lowercase letters and underscores for multi-word variable names.


NEW QUESTION # 50
The number of phone calls that call center receives in a day is an example of:

  • A. continuous data.
  • B. ordinal data.
  • C. discrete data.
  • D. categorical data.

Answer: C


NEW QUESTION # 51
Melissa wants to explore central tendency in her dataset.
Which statistic best matches her need?

  • A. Standard deviation.
  • B. Interquartile range.
  • C. Range.
  • D. Median.

Answer: D

Explanation:
Correct answer C. Median.
The Median is the middle observation of variable and is, therefore, a measure of central tendency.
Interquartile range is a measure of position.
Range and Standard deviation are both measures of dispersion.


NEW QUESTION # 52
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