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CompTIA Data+ Certification Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
A data analyst was asked to create a chart that shows the relationship between study hours and exam scores for each student using the data sets in the table below:

Which of the following charts would BEST represent the relationship between the variables?

Answer: A

Explanation:
Explanation
This is because a scatter plot is a type of chart that shows the relationship between two variables for each observation or unit in a data set, such as study hours and exam scores for each student in this case. A scatter plot can be used to display and analyze the correlation, trend, or pattern among the variables, as well as identify any outliers or clusters in the data. For example, a scatter plot can show if there is a positive, negative, or no correlation between study hours and exam scores, as well as show if there are any students who have unusually high or low exam scores compared to their study hours. The other charts are not the best charts to represent the relationship between the variables. Here is why:
A histogram is a type of chart that shows the frequency or the count of values in a single variable for different intervals or bins, such as exam scores for different ranges in this case. A histogram can be used to display and analyze the distribution, shape, or spread of the variable, as well as identify any gaps, peaks, or skewness in the data. For example, a histogram can show if most students have high, low, or average exam scores, as well as show if there are any intervals that have no students at all.
A heat map is a type of chart that shows the intensity or the magnitude of values in two variables for different categories or groups, such as exam scores and study hours for different student names in this case. A heat map can be used to display and analyze the variation, contrast, or comparison among the categories or groups, as well as identify any hot spots, cold spots, or gradients in the data. For example, a heat map can show which students have higher or lower exam scores and study hours than others, as well as show if there is a color pattern that indicates a relationship between exam scores and study hours.
A bar chart is a type of chart that shows the value or the amount of a single variable for different categories or groups, such as exam scores for different student names in this case. A bar chart can be used to display and analyze the comparison, ranking, or proportion among the categories or groups, as well as identify any differences, similarities, or outliers in the data. For example, a bar chart can show which students have higher or lower exam scores than others, as well as show if there are any students who have exceptionally high or low exam scores.


NEW QUESTION # 135
An analyst has generated a report that includes the number of months in the first two quarters of 2019 when sales exceeded $50,000:

Which of the following functions did the analyst use to generate the data in the Sales_indicator column?

Answer: D


NEW QUESTION # 136
Which of the following variable name formats would be problematic if used in the majority of data software programs?

Answer: A


NEW QUESTION # 137
While reviewing survey data, a research analyst notices data is missing from all the responses to a single question. Which of the following methods would BEST address this issue?

Answer: B

Explanation:
Explanation
This is because missing data is a type of data quality issue that occurs when data is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis or process. Missing data can be caused by various factors, such as human error, system error, or non-response. Missing data can be addressed by using various methods, such as replacing missing data, which means filling in or imputing the missing values with some reasonable estimates, such as mean, median, mode, or regression. The other methods are not used to address missing data. Here is why:
Remove duplicate data is a type of method that eliminates or reduces duplicate data, which is a type of data quality issue that occurs when data is repeated or copied in a data set. Removing duplicate data does not address missing data, but rather affects the quantity and validity of the data.
Replace redundant data is a type of method that eliminates or reduces redundant data, which is a type of data quality issue that occurs when data is unnecessary or irrelevant for the analysis or purpose.
Replacing redundant data does not address missing data, but rather affects the efficiency and performance of the analysis or process.
Remove invalid data is a type of method that eliminates or reduces invalid data, which is a type of data quality issue that occurs when data is incorrect or inaccurate in a data set. Removing invalid data does not address missing data, but rather affects the validity and reliability of the analysis or process.


NEW QUESTION # 138
Which of the following is the best reason for removing data outliers?

Answer: D

Explanation:
Outliers are data points that differ significantly from other observations and can skew analysis results, leading to inaccurate conclusions. The main reason to remove them is when their values vary so significantly from the rest of the data that they distort statistical calculations or trends.
CompTIA Data+ Reference:
CompTIA Data+ Study Guide (Exam DA0-001), Chapter 5: Data Cleaning and Preparation, Section
"Identifying and Handling Outliers", Official CompTIA CertMaster Learn for Data+, Module 5.1 "Data Quality Issues: Outliers".


NEW QUESTION # 139
......

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