CompTIA DA0-001 Online Questions & Answers

• Question 211:

  A data analyst is designing a dashboard that will provide a story of sales and determine which site is providing the highest sales volume per customer. The analyst must choose an appropriate chart to include in the dashboard. The following data is available:

  

  Which of the following types of charts should be considered?

  A. Include a line chart using the site and average sales per customer.
  B. Include a pie chart using the site and sales to average sales per customer.
  C. Include a scatter chart using sales volume and average sales per customer.
  D. Include a column chart using the site and sales to average sales per customer.
  C. Include a scatter chart using sales volume and average sales per customer.

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  Explanation

  A scatter chart using sales volume and average sales per customer is the best type of chart to include in the dashboard. A scatter chart is a type of chart that displays the relationship between two numerical variables using dots or markers. A scatter chart can show how one variable affects another, how strong the correlation is between them, and how the data points are distributed. In this case, a scatter chart can show the story of sales and determine which site is providing the highest sales volume per customer by plotting the sales volume on the x-axis and the average sales per customer on the y-axis. Each dot on the chart will represent a site, and the analyst can easily compare the sites based on their position on the chart. A site with a high sales volume and a high average sales per customer will be in the upper right quadrant, indicating a high performance. A site with a low sales volume and a low average sales per customer will be in the lower left quadrant, indicating a low performance. A site with a high sales volume and a low average sales per customer will be in the lower right quadrant, indicating a high volume but low value. A site with a low sales volume and a high average sales per customer will be in the upper left quadrant, indicating a low volume but high value. A scatter chart can also show if there is a positive or negative correlation between the two variables, or if there is no correlation at all. A positive correlation means that as one variable increases, so does the other. A negative correlation means that as one variable increases, the other decreases. No correlation means that there is no relationship between the two variables.

  The other types of charts are not as suitable for this purpose. A line chart is a type of chart that displays the change of one or more variables over time using lines. A line chart can show trends, patterns, and fluctuations in the data. However, in this case, there is no time variable involved, so a line chart would not be appropriate. A pie chart is a type of chart that displays the proportion of each category in a whole using slices of a circle. A pie chart can show how each category contributes to the total and compare the relative sizes of each category. However, in this case, there are two numerical variables involved, so a pie chart would not be able to show their relationship. A column chart is a type of chart that displays the comparison of one or more variables across categories using vertical bars. A column chart can show how each category differs from each other and rank them by size. However, in this case, a column chart would not be able to show the relationship between sales volume and average sales per customer, as it would only show one variable for each site.

• Question 212:

  Which of the following would a data analyst look for first if 100% participation is needed on survey results?

  A. Missing data
  B. Invalid data
  C. Redundant data
  D. Duplicate data
  A. Missing data

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  Explanation

  Missing data is a type of data quality issue that occurs when some values in a data set are not recorded or available. Missing data can affect the validity and reliability of survey results, especially if the missing values are not random or ignorable. Missing data can also reduce the sample size and the statistical power of the analysis. If 100% participation is needed on survey results, a data analyst would look for missing data first, because missing data would indicate that some participants did not complete or submit the survey, or that some responses were not recorded or transmitted correctly. A data analyst would need to identify the causes and patterns of missing data, and apply appropriate methods to handle or prevent missing data, such as imputation, deletion, weighting, or follow-up.

• Question 213:

  A report is scheduled to run and be distributed at the end of business each day. On Mondays, one of the recipients opens the previous week's reports and combines them to calculate the weekly totals and projections for the coming week. This is a tedious process, and the recipient asks an analyst for help.

  Which of the following should the analyst recommend?

  A. Add calculation fields to the daily report so the totals are built in.
  B. Create a new report with weekly totals set to run at the end of business on Friday.
  C. Provide a daily summary to the report with totals to save the user the effort of manual calculations.
  D. Reduce the frequency of the report to once a week and change the date range.
  B. Create a new report with weekly totals set to run at the end of business on Friday.

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  Explanation

  Creating a new report that automatically calculates weekly totals would streamline the process for the recipient. By setting this report to run at the end of business on Friday, it would provide the recipient with the necessary information for the

  entire week in one consolidated document. This eliminates the need for manual calculations and combines the previous week's data into one report, making it more efficient and less time- consuming.

  References:

  Best practices in business analytics suggest automating repetitive tasks and consolidating reports where possible to improve efficiency and reduce the potential for human error.

• Question 214:

  A data analyst for a media company needs to determine the most popular movie genre. Given the table below:

  

  Which of the following must be done to the Genre column before this task can be completed?

  A. Append
  B. Merge
  C. Concatenate
  D. Delimit
  D. Delimit

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  Explanation

  Delimiting is the process of splitting a column of data into multiple columns based on a separator or delimiter character. Delimiting can help separate data that is combined or concatenated in one column into distinct values or categories. For

  example, if a column contains text values that are separated by commas, such as "Comedy, Suspense", delimiting can split this column into two columns, one for "Comedy" and one for "Suspense". Delimiting is different from other options,

  such as appending, merging, or concatenating, which are methods of combining or joining data from multiple columns or sources. In this case, the data analyst needs to determine the most popular movie genre based on the Genre column in

  the table. However, this column contains multiple genres for each movie, separated by commas. Therefore, the data analyst must delimit this column before this task can be completed. Therefore, the correct answer is

  D.

  References:

  Split text into different columns with functions - Office Support, How to Split Text in Excel (Using Formulas and Split Function)

• Question 215:

  Which of the following activities occurs during the ETL process?

  A. Reviewing and addressing missing values
  B. Creating a dashboard
  C. Inserting a pivot table and pivot chart
  D. Multiplying unique data
  A. Reviewing and addressing missing values

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  Explanation

• Question 216:

  Which one of the following is a measure of dispersion?

  A. Variance.
  B. Mode.
  C. Median.
  D. Mean.
  A. Variance.

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  Explanation

• Question 217:

  A junior web developer is developing a new application where users can upload short videos. The first task is to create a homepage that shows the headline "Upload Your Short Videos" and a clickable button that says "upload now".

  Which of the following HTML commands would help the developer to complete the task successfully?

  A. < span >Upload Your Short Videos< /span >< button >upload now< /button >
  B. < p >Upload Your Short Videos< /p >< p >upload now< /p >
  C. < hl >Upload Your Short Videos< /h1 >< button >upload now< /button >
  D. < hl >Upload Your Short Videos< /h1 >< hl >upload now< /h1 >
  C. < hl >Upload Your Short Videos< /h1 >< button >upload now< /button >

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  Explanation

  The HTML commands that would help the developer to complete the task successfully are

  Upload Your Short Videos

  and upload now. The

  tag defines a heading level 1, which is the largest and most

  important heading on a webpage. The tag defines a clickable button that can perform some action when clicked. The other options are not suitable for the task, as they either use the wrong tags or do not create a clickable button.

  The tag defines a section of text with no specific meaning or formatting. The

  tag defines a paragraph of text. The tag does not exist in HTML.

  HTML Tags - W3Schools

• Question 218:

  A data analyst received a large amount of third-party data that needs to be joined with in- house data files. After the data is joined, the analyst notices three columns all contain dates.

  Which of the following should the analyst do to maintain data consistency?

  A. Append all date columns and parse the strings.
  B. Impute all three date columns and then merge.
  C. Merge all date columns and unify the format.
  D. Separate the columns into a table and merge.
  C. Merge all date columns and unify the format.

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  Explanation

  When dealing with multiple date columns from different data sources, it's crucial to ensure consistency and accuracy in the dataset. The best practice is to merge the date columns and standardize the date format across the entire dataset.

  This approach helps maintain data integrity, simplifies analysis, and avoids confusion that could arise from having multiple date formats. Unifying the date format is particularly important when the data will be used for time series analysis or

  when dates are key to joining with other datasets.

  References:

  Best practices in data merging emphasize the importance of a single point of reference and the need to avoid data loss or damage to individual data structures. Power BI guides suggest that merging columns should be done carefully to

  maintain data integrity and avoid errors and inconsistencies.

  Oracle Blogs highlight the need for a consistent number of columns among data sources when combining data with unions.

  Excel tutorials recommend organizing data before merging and using formulas for complex merges.

  An Excel guide on merging date and time columns advises employing functions to ensure seamless handling of non-date values.

• Question 219:

  Which of the following descriptive statistical methods are measures of central tendency? (Choose two.)

  A. Mean
  B. Minimum
  C. Mode
  D. Variance
  E. Correlation
  F. Maximum
  A. Mean
  C. Mode

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  Explanation

  Mean and mode are measures of central tendency, which describe the typical or most common value in a distribution of data. Mean is the arithmetic average of all the values in a dataset, calculated by adding up all the values and dividing by the number of values. Mode is the most frequently occurring value in a dataset. Other measures of central tendency include median, which is the middle value when the data is sorted in ascending or descending order.

• Question 220:

  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
  A. Involves the use of descriptive statistics to understand observations

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  Explanation

  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 them.