BEST COMPTIA DA0-001 PRACTICE | DA0-001 NEW GUIDE FILES

Best CompTIA DA0-001 Practice | DA0-001 New Guide Files

Best CompTIA DA0-001 Practice | DA0-001 New Guide Files

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Tags: Best DA0-001 Practice, DA0-001 New Guide Files, Latest DA0-001 Exam Materials, Clear DA0-001 Exam, Pdf DA0-001 Pass Leader

We provide up-to-date CompTIA Data+ Certification Exam (DA0-001) exam questions and study materials in three different formats. We have developed three variations of authentic CompTIA DA0-001 exam questions to cater to different learning preferences, ensuring that all candidates can effectively prepare for the DA0-001 Practice Test. ValidDumps offers CompTIA Data+ Certification Exam (DA0-001) practice questions in PDF format, browser-based practice exams, and desktop practice test software.

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CompTIA Data+ Certification Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
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. Median
  • C. Standard deviation
  • D. Mode

Answer: B

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 # 64
Refer to the exhibit.
Given the image below:

The data should be cleaned because of the presence of:

  • A. invalid data.
  • B. outlier
  • C. non-parametric data.
  • D. multicollinearity.

Answer: B

Explanation:
The answer is A. Outlier.
Short explanation: An outlier is a data point that differs significantly from the rest of the data in a dataset. An outlier can indicate an error, an anomaly, or a rare event in the data. An outlier can affect the statistical analysis and visualization of the data, such as skewing the mean, variance, or distribution of the data. Therefore, data should be cleaned to identify and remove or correct any outliers.
The image below shows a box plot graph with a vertical axis labeled "Customer Calls" and a horizontal axis labeled "Churn". The box plot is blue in color and the median value is around 2. There are 7 outliers above the box plot, ranging from 4 to 8.
image)
A box plot is a type of graph that can show the distribution of data values using five summary statistics: minimum, maximum, median, first quartile, and third quartile. The box represents the interquartile range (IQR), which is the difference between the first and third quartiles. The median is shown as a line inside the box. The whiskers extend from the box to the minimum and maximum values, excluding any outliers. Outliers are shown as dots or circles outside the whiskers.
In this graph, we can see that most of the customer calls are between 0 and 4, with a median of 2. However, there are 7 outliers that have more than 4 customer calls, up to 8. These outliers may indicate some customers who have more issues or complaints than others, or some errors or anomalies in the data collection or recording process. These outliers can affect the analysis and interpretation of the customer calls and churn relationship, such as making it seem that more customer calls lead to less churn, which may not be true for the majority of the customers. Therefore, data should be cleaned to investigate and handle these outliers appropriately.


NEW QUESTION # 65
Which of the following are reasons to create and maintain a data dictionary? (Choose two.)

  • A. To remember specifics about data fields
  • B. To specify user groups for databases
  • C. To provide continuity through personnel turnover
  • D. To reduce processing power requirements
  • E. To confine breaches of PHI data
  • F. To improve data acquisition

Answer: A,F


NEW QUESTION # 66
Which of the following best describes an exploratory analysis?

  • A. Involves the use of arithmetic algebra to determine the distribution
  • B. Involves analysis of exploring data sets for performance tracking
  • C. Involves the use of descriptive statistics to understand observations
  • D. Involves the testing of specific hypotheses

Answer: C

Explanation:
A) Involves the use of descriptive statistics to understand observations.
Exploratory data analysis (EDA) is a method of analyzing and investigating data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. EDA involves the use of descriptive statistics, such as mean, median, mode, standard deviation, frequency, or percentage, to understand the distribution, central tendency, variability, and relationship of the data. EDA helps to see what the data can reveal beyond the formal modeling or hypothesis testing, and provides a better understanding of data set variables and the interactions between them1.


NEW QUESTION # 67
Given the image below:

The data should be cleaned because of the presence of:

  • A. invalid data.
  • B. outlier
  • C. non-parametric data.
  • D. multicollinearity.

Answer: B

Explanation:
Explanation
The answer is A. Outlier.
Short explanation: An outlier is a data point that differs significantly from the rest of the data in a dataset. An outlier can indicate an error, an anomaly, or a rare event in the data. An outlier can affect the statistical analysis and visualization of the data, such as skewing the mean, variance, or distribution of the data.
Therefore, data should be cleaned to identify and remove or correct any outliers.
The image below shows a box plot graph with a vertical axis labeled "Customer Calls" and a horizontal axis labeled "Churn". The box plot is blue in color and the median value is around 2. There are 7 outliers above the box plot, ranging from 4 to 8.
image)
A box plot is a type of graph that can show the distribution of data values using five summary statistics:
minimum, maximum, median, first quartile, and third quartile. The box represents the interquartile range (IQR), which is the difference between the first and third quartiles. The median is shown as a line inside the box. The whiskers extend from the box to the minimum and maximum values, excluding any outliers. Outliers are shown as dots or circles outside the whiskers.
In this graph, we can see that most of the customer calls are between 0 and 4, with a median of 2. However, there are 7 outliers that have more than 4 customer calls, up to 8. These outliers may indicate some customers who have more issues or complaints than others, or some errors or anomalies in the data collection or recording process. These outliers can affect the analysis and interpretation of the customer calls and churn relationship, such as making it seem that more customer calls lead to less churn, which may not be true for the majority of the customers. Therefore, data should be cleaned to investigate and handle these outliers appropriately.


NEW QUESTION # 68
......

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