CompTIA CompTIA Data+ DA0-001 Questions & Answers

• Question 11:

  An analyst is preparing a report that contains weather data. The temperatures are shown in Fahrenheit. but they must be reported in Celsius. Which of the following should the analyst do to fix this issue?

  A. Normalize the data.

  B. Standardize the data.

  C. Rescale the data.

  D. Aggregate the data.

  Correct Answer: C

  The analyst should rescale the data to fix this issue. Rescaling is a process of transforming data from one scale to another, such as changing the units of measurement. In this case, the analyst needs to rescale the temperatures from Fahrenheit to Celsius, which are two different scales for measuring temperature. To do this, the analyst can use the following formula: Celsius = (Fahrenheit - 32) * 5/9 This formula converts each temperature value from Fahrenheit to Celsius by subtracting 32 and multiplying by 5/9. For example, if the temperature is 68

• Question 12:

  Under which of the following circumstances should the null hypothesis be accepted when a = 0.05?

  A. When p is 0.00003

  B. When p is 0.001

  C. When p is 0.04

  D. When p is 0.06

  Correct Answer: D

  The null hypothesis should be accepted when the p-value is greater than the alpha level, which is the significance level of the test. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed in the sample, assuming that the null hypothesis is true. The alpha level is the probability of rejecting the null hypothesis when it is true, which is also known as a type I error12. In this case, the alpha level is 0.05, which means that there is a 5% chance of rejecting the null hypothesis when it is true. Therefore, to reject the null hypothesis, the p-value must be less than or equal to 0.05, which indicates that the test statistic is very unlikely to occur by chance under the null hypothesis. Conversely, to accept the null hypothesis, the p-value must be greater than 0.05, which indicates that the test statistic is not very unlikely to occur by chance under the null hypothesis. Among the four options, only option D has a p-value that is greater than 0.05 (p = 0.06). Therefore, option D is the correct answer. When p = 0.06, it means that there is a 6% chance of obtaining a test statistic at least as extreme as the one observed in the sample, assuming that the null hypothesis is true. This probability is not very low, and therefore does not provide enough evidence to reject the null hypothesis.

• Question 13:

  An analyst needs to provide a chart to identify the composition between the categories of the survey response data set:

  

  Which of the following charts would be BEST to use?

  A. Histogram

  B. Pie

  C. Line

  D. Scatter pot

  E. Waterfall

  Correct Answer: B

  Explanation: The best chart to use to identify the composition between the categories of the survey response data set is a pie chart. A pie chart is a circular chart that shows the relative proportions of different categories in a whole. A pie chart is divided into slices that represent the percentage or frequency of each category. A pie chart is suitable for displaying categorical data that has a few categories and does not have any hierarchical or temporal relationship. In this case, a pie chart can show the composition of the favorite colors among the survey respondents, as well as the percentage of each color. The other options are not as good as a pie chart for this purpose, as they are more suitable for displaying numerical data that has some kind of distribution, trend, correlation, or comparison. A histogram is a bar chart that shows the frequency distribution of a single numerical variable. A line chart is a chart that shows the change of one or more numerical variables over time or another continuous variable. A scatter plot is a chart that shows the relationship between two numerical variables by plotting them as points on a Cartesian plane. A waterfall chart is a chart that shows how an initial value is increased or decreased by a series of intermediate values, resulting in a final value. Reference: [Choosing the Right Chart Type - DataCamp]

• Question 14:

  Which of the following best describes how discrete data differs from continuous data?

  A. Discrete data cannot create a sloped line.

  B. Discrete data can only be a finite number of values.

  C. Discrete data can have decimal points.

  D. Discrete data applies only to numbers.

  Correct Answer: B

  Explanation: Discrete data are data that can only assume specific values that are countable and distinct. For example, the number of books, the number of heads in a coin toss, or the number of patients in a hospital are discrete data. Discrete data cannot have fractional or decimal values, and there are clear spaces between the possible values12. Continuous data are data that can assume any value within a range and can be meaningfully divided into smaller parts. For example, the weight, height, length, time, or temperature are continuous data. Continuous data can have fractional or decimal values, and there are infinite numbers of possible values between any two points12.

• Question 15:

  A data analyst is developing a dashboard to track and monitor metrics. Which of the following best practices should be taken into during the FIRST pment process?

  A. Create a A Aupirarrame:

  B. Deploy to production.

  C. Copy a dashboard design from the Internet.

  D. Develop a dashboard.

  Correct Answer: A

  Explanation: A dashboard is a graphical display that summarizes and presents key performance indicators (KPIs) and metrics for a business or a project. A dashboard should be clear, concise, and easy to understand. To develop a dashboard, one of the best practices is to create a wireframe or a mockup first. A wireframe or a mockup is a low- fidelity sketch or prototype of the dashboard layout and design, which helps to define the scope, requirements, and functionality of the dashboard. Creating a wireframe or a mockup can help to save time and resources, as well as to get feedback from stakeholders and users before deploying the dashboard to production. Therefore, the correct answer is A. References: [Dashboard Design Best Practices: 4 Key Principles | Toptal], [How to Create an Effective Dashboard (with Examples) | Tableau]

• Question 16:

  Which of the following value is the measure of dispersion "range" between the scores of ten students in a test. The scores of ten students in a test are 17, 23, 30, 36, 45, 51, 58, 66, 72, 77.

  A. 90

  B. 60

  C. 70

  D. 80

  Correct Answer: B

  The correct answer is: 60 Range is the interval between the highest and the lowest score. Range is a measure of variability or scatteredness of the varieties or observations among themselves and does not give an idea about the spread of the observations around some central value. Symbolically R = Hs - Ls. Where R = Range; Hs is the 'Highest score' and Ls is the Lowest Score. The scores of ten students in a test are: 17, 23, 30, 36, 45, 51, 58, 66, 72, 77. The highest score is 77 and the lowest score is 17. So the range is the difference between these two scores Range = 77 - 17 = 60

• Question 17:

  The process of performing initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization is called:

  A. a t-test.

  B. a performance analysis.

  C. an exploratory data analysis.

  D. a link analysis.

  Correct Answer: C

  Explanation: This is because exploratory data analysis is a type of process that performs initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization, such as box plots, histograms, scatter plots, etc. Exploratory data analysis can be used to understand and summarize the data, as well as to generate hypotheses or questions for further analysis or research. For example, exploratory data analysis can be used to identify and visualize the characteristics, features, or behaviors of the data, as well as to measure their distribution, frequency, or correlation. The other options are not types of processes that perform initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization. Here is what they mean:

  A t-test is a type of statistical method that tests whether there is a significant difference between the means of two groups or samples, such as whether there is a difference between the average exam scores of two classes in this case. A t-test can be used to test or verify a claim or an assumption about the data, as well as to measure the confidence or the error of the estimation. A performance analysis is a type of process that measures whether the data meets certain goals or objectives, such as targets, benchmarks, or standards. A performance analysis can be used to identify and visualize the gaps, deviations, or variations in the data, as well as to measure the efficiency, effectiveness, or quality of the outcomes. For example, a performance analysis can be used to determine if there is a gap between a student's test score and their expected score based on their previous performance. A link analysis is a type of process that determines whether the data is connected to other datapoints, such as entities, events, or relationships. A link analysis can be used to identify and visualize the patterns, networks, or associations among the datapoints, as well as to measure the strength, direction, or frequency of the connections. For example, a link analysis can be used to determine if there is a connection between a customer's purchase history and their loyalty program status.

• Question 18:

  Which one of the following would not normally be considered a summary statistic?

  A. z-score.

  B. Mean.

  C. Variance.

  D. Standard deviation.

  Correct Answer: A

  Simply put, a z-score (also called a standard score) gives you an idea of how far from the mean a data point is. But more technically it's a measure of how many standard deviations below or above the population mean a raw score is. A z-score can be placed on a normal distribution curve.

• Question 19:

  Which of the following best describes an exploratory analysis?

  A. Involves the use of descriptive statistics to understand observations

  B. Involves analysis of exploring data sets for performance tracking

  C. Involves the testing of specific hypotheses

  D. Involves the use of arithmetic algebra to determine the distribution

  Correct Answer: A

  Answer: 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.

• Question 20:

  What subset of Structured Query Language (SQL) is used to add, remove, modify, or retrieve the information stored within a relational database?

  A. DDL.

  B. DSL.

  C. DQL.

  D. DML.

  Correct Answer: D

  Correct answer D. DML.

  The Data Manipulation Language (DML) is used to work with the data stored in a database. DML includes the SELECT, INSERT, UPDATE, and DELETE commands. The Data Definition Language (DDL) contains the commands used to

  create and structure a relational database. It includes the CREATE, ALTER, and DROP commands.

  DDL and DML are the only two sublanguages of SQL.