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CompTIA DA0-001, also known as the CompTIA Data+ Certification Exam, is a certification exam that measures an individual's knowledge and skills in data management. CompTIA Data+ Certification Exam certification exam is designed for individuals who want to demonstrate their expertise in data management, data analysis, and data governance. DA0-001 exam covers a wide range of topics such as data storage, data security, data analysis, and data governance.

CompTIA DA0-001, also known as the CompTIA Data+ Certification Exam, is a vendor-neutral certification exam that is designed to test the knowledge and skills of IT professionals in the field of data management. CompTIA Data+ Certification Exam certification is aimed at individuals who are responsible for managing and analyzing data, such as database administrators, data analysts, and data architects. DA0-001 Exam covers a wide range of topics, including data storage and management, data analysis and visualization, data security and privacy, and data governance and compliance.

CompTIA Data+ Certification Exam Sample Questions (Q295-Q300):

NEW QUESTION # 295
Which of the following would be the best way to identify multicollinear attributes in a data set?

A. Correlation coefficient
B. Two-sample f-test
C. Two-way ANOVA
D. Chi-squared test

Answer: A

NEW QUESTION # 296
An analyst is working with the income data of suburban families in the United States. The data set has a lot of outliers, and the analyst needs to provide a measure that represents the typical income. Which of the following would BEST fulfill the analyst's goal?

A. Mean
B. Standard deviation
C. Mode
D. Median

Answer: D

Explanation:
his is because median is a type of statistical measure that represents the typical value or central tendency of a data set, which means that it divides the data set into two equal halves, such that half of the values are above it and half are below it. Median can be used to provide a measure that represents the typical income of suburban families in the United States, especially when the data set has a lot of outliers, which means that it has values that are unusually high or low compared to the rest of the data set. Median can provide a measure that represents the typical income of suburban families in the United States, because it is not affected or skewed by the outliers, as it only depends on the middle value or the middle two values of the data set, regardless of how extreme or distant the outliers are. For example, median can provide a measure that represents the typical income of suburban families in the United States, by finding the income value that splits the data set into two equal groups of families, such that 50% of the families have higher incomes and 50% have lower incomes. The other statistical measures are not the best measures to represent the typical income of suburban families in the United States. Here is why:
Mean is a type of statistical measure that represents the average value or central tendency of a data set, which means that it is the sum of all the values divided by the number of values. Mean is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is affected or skewed by the outliers, as it takes into account all the values in the data set, regardless of how extreme or distant they are. For example, mean can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is influenced by a few very high or very low incomes, which could make it higher or lower than most of the incomes in the data set.
Mode is a type of statistical measure that represents the most frequent value or mode of a data set, which means that it is the value that occurs most often in the data set. Mode is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is not representative or indicative of the central tendency or distribution of the data set, as it only depends on the count or occurrence of a single value or a few values in the data set, regardless of how common or rare they are. For example, mode can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is repeated more often than others, which could be an outlier or an anomaly in the data set.
Standard deviation is a type of statistical measure that represents the amount of dispersion or variation of a data set, which means that it quantifies how much the values in a data set vary or deviate from the mean or average of the data set. Standard deviation is not a measure that represents the typical income of suburban families in the United States, but rather a measure that describes the spread or distribution of their incomes, as well as identifies any outliers or extreme values in their incomes. For example, standard deviation can provide a measure that describes how diverse or homogeneous their incomes are, as well as how far their incomes are from their average income.

NEW QUESTION # 297
The duration of a phone call in milliseconds is an example of:

A. continuous data.
B. boolean data.
C. ordinal data.
D. nominal data.

Answer: A

Explanation:
Explanation
The correct answer is D. Continuous data.
Continuous data is a type of quantitative data that can take any value within a range and can be measured with infinite precision. Continuous data can be expressed as fractions, decimals, or percentages. Examples of continuous data are height, weight, temperature, time, speed, etc12 The duration of a phone call in milliseconds is an example of continuous data, because it can take any value within a range (from zero to infinity) and can be measured with infinite precision (up to milliseconds or even smaller units). The duration of a phone call in milliseconds can also be expressed as fractions, decimals, or percentages of a larger unit (such as seconds, minutes, or hours).
Ordinal data is not correct, because ordinal data is a type of qualitative or categorical data that can be ordered or ranked according to some criterion. Ordinal data can have a logical order, but the intervals between the values are not equal or meaningful. Examples of ordinal data are grades, ratings, ranks, etc12 Nominal data is not correct, because nominal data is a type of qualitative or categorical data that can be labeled or named without any order or ranking. Nominal data can have a finite number of categories or classes, but the categories have no intrinsic value or hierarchy. Examples of nominal data are gender, color, nationality, etc12 Boolean data is not correct, because boolean data is a type of binary data that can have only two possible values: true or false. Boolean data can be used to represent logical statements, conditions, or outcomes.
Examples of boolean data are yes/no, on/off, 1/0, etc.

NEW QUESTION # 298
What data obfuscation technique is designed to be reversible?

A. Deletion
B. Hashing
C. Masking
D. Tokenization

Answer: D

NEW QUESTION # 299
A data analyst reviews the following data set:

Which of the following is the range value?

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

Answer: A

NEW QUESTION # 300
......

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