CompTIA DA0-001 Online Practice Questions and Exam Preparation

CompTIA DA0-001 Online Questions & Answers

• Question 141:

  The number of phone calls that the call center receives in a day is an example of:

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

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  Explanation

  Discrete data is a type of data that can only take certain values, usually whole numbers or integers. Discrete data can be counted, but not measured. For example, the number of students in a class, the number of books in a library, or the

  number of phone calls that a call center receives in a day are all examples of discrete data. Discrete data is different from continuous data, which can take any value within a range, and can be measured with precision. For example, the

  height of a person, the weight of a fruit, or the temperature of a room are all examples of continuous data. Therefore, the correct answer is

  D.

  References:

  [Discrete vs Continuous Data: Definition and Examples - Statistics How To], [Discrete Data - Definition and Examples | Math Goodies]

• Question 142:

  A business intelligence engineer needs to reduce the size of a data model for reporting purposes. The data set contains more than one million rows, and the table has a date-time column named Date. Which of the following should the analyst do to complete this task?

  A. Change the data type of the Date column to text.
  B. Trim the date.
  C. Round the hour of the Date column to the start of the hour.
  D. Split the Date column into two columns--time and date.
  C. Round the hour of the Date column to the start of the hour.

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  Explanation

• Question 143:

  Given the table below:

  

  Which of the following variables can be considered inconsistent, and how many distinct values should the variable have?

  A. Name, one
  B. Gender, two
  C. Level, three
  D. Code, four
  E. Region, five
  B. Gender, two

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  Explanation

  The table provided shows an inconsistency in the `Gender' column, which lists three distinct values: Male, Female, and College. This is inconsistent because `College' is not a gender category. The `Gender' column should only have two

  distinct values, typically `Male' and `Female', to accurately represent gender data. This error could be due to a data entry mistake or a misclassification during data collection. In data analysis, it's crucial to ensure that categorical variables like

  gender are consistent and correctly classified, as this can significantly impact the analysis results. Data cleaning processes often involve identifying and correcting such inconsistencies to maintain the integrity of the data set.

  References:

  Data quality management principles emphasize the importance of consistency in data values, especially for categorical variables like gender.

  Best practices in data cleaning include checking for and rectifying inconsistencies or misclassifications in data sets.

  The importance of accurate data classification is highlighted in data analysis literature, as it directly affects the validity of the analysis results.

• Question 144:

  Which of the following statements would be used to append two tables that have the same number of columns?

  A. UNION ALL
  B. MERGE
  C. GROUP BY
  D. JOIN
  A. UNION ALL

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  Explanation

  The correct answer is A. UNION ALL.

  UNION ALL is a SQL statement that appends two tables that have the same number of columns and compatible data types. UNION ALL preserves all the rows from both tables, including any duplicates. Option B: MERGE is not correct,

  because MERGE is a SQL statement that combines the data of two tables based on a common column. MERGE can perform insert, update, or delete operations on the target table depending on the matching or non-matching rows from the

  source table.

  Option C: GROUP BY is not correct, because GROUP BY is a SQL clause that groups the rows of a table based on one or more columns. GROUP BY is often used with aggregate functions, such as SUM, AVG, COUNT, etc., to calculate

  summary statistics for each group.

  Option D: JOIN is not correct, because JOIN is a SQL clause that combines the data of two tables based on a common column or condition. JOIN can produce different results depending on the type of join, such as INNER JOIN, LEFT JOIN,

  RIGHT JOIN, etc.

• Question 145:

  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.
  C. an exploratory data analysis.

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  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 146:

  Consider this dataset showing the retirement age of 11 people, in whole years:

  54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60

  This tables show a simple frequency distribution of the retirement age data.

  

  A. 56
  B. 55
  C. 57
  D. 54
  D. 54

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  Explanation

  A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution. There

  are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical or central value in the distribution.

  What is the mode?

  The mode is the most commonly occurring value in a distribution. The most commonly occurring value is 54, therefore the mode of this distribution is 54 years.

• Question 147:

  A gambler thinks that a coin is fair and is equally likely to turn up heads or tails when the coin is flipped. Which of the following tests should the gambler use to fest this hypothesis?

  A. t-test
  B. Chi-squared test
  C. Rank sum test
  D. Ratio test
  B. Chi-squared test

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  Explanation

• Question 148:

  A data set for sales per month includes the following data:

  

  Which of the following cleaning and profiling methods should be applied to the data set?

  A. Data outliers
  B. Invalid data
  C. Duplicate data
  D. Data type validation
  B. Invalid data

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  Explanation

• Question 149:

  A data analyst received the information in the table below from a recently completed marketing campaign:

  

  Which of the following is the total order conversion rate?

  A. 13.2%
  B. 14.8%
  C. 22.3%
  D. 85.2%
  B. 14.8%

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  Explanation

  The correct answer is A. 13.2%.

  The total order conversion rate is the ratio of the total number of orders to the total number of clicks, expressed as a percentage. To calculate the total order conversion rate, we need to sum up the clicks and orders from all the channels, and

  then divide the orders by the clicks and multiply by 100.

  Using the data from the table, we can do the following:

  Total clicks = 580 + 800 + 1,200 + 300 + 620 = 3,500 Total orders = 55 + 100 + 220 + 60 + 85 = 520

  Total order conversion rate = (520 / 3,500) x 100 = 14.857% Rounding to one decimal place, we get 14.9%

  Therefore, the total order conversion rate is 14.9%.

• Question 150:

  An analyst needs to conduct a quick analysis. Which of the following is the FIRST step the analyst should perform with the data?

  A. Conduct an exploratory analysis and use descriptive statistics.
  B. Conduct a trend analysis and use a scatter chart.
  C. Conduct a link analysis and illustrate the connection points.
  D. Conduct an initial analysis and use a Pareto chart.
  A. Conduct an exploratory analysis and use descriptive statistics.

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  Explanation

  The first step the analyst should perform with the data is to conduct an exploratory analysis and use descriptive statistics. Exploratory analysis is a type of analysis that aims to summarize the main characteristics of the data, identify patterns, outliers, and relationships, and generate hypotheses for further investigation. Descriptive statistics are numerical measures that describe the central tendency, variability, and distribution of the data, such as mean, median, mode, standard deviation, range, quartiles, etc. Exploratory analysis and descriptive statistics can help the analyst gain a better understanding of the data and its quality, as well as prepare the data for further analysis.