CompTIA CompTIA Certifications DA0-001 Questions & Answers

• Question 71:

  Which of the following reports can be used when insight into operational performance is needed each Wednesday?

  A. Static report

  B. Tactical report

  C. Recurring report

  D. Ad hoc report

  Correct Answer: C

• Question 72:

  Randy scored 76 on a math test, Katie scored 86 on a science test, Ralph scored 80 on a history test, and Jean scored 80 on an English test. The table below contains the mean and standard deviation of the scores for each of the courses:

  

  Using this information, which of the following students had the BEST score?

  A. Randy

  B. Katie

  C. Ralph

  D. Jean

  Correct Answer: B

  Explanation: To compare the students' scores, we need to standardize them by using the z-score formula, which is:

  z = (x - ) /

  where x is the raw score, is the mean, and is the standard deviation. The z-score tells us how many standard deviations a score is above or below the mean. A higher z-score means a better score relative to the average.

  Using the table, we can calculate the z-scores for each student as follows:

  Randy: z = (76 - 70) / 2 = 3 Katie: z = (86 - 80) / 3 = 2 Ralph: z = (80 - 75) / 2 = 2.5 Jean: z = (80 - 90) / 1 = -10

  The student with the highest z-score is Randy, with a z-score of 3. This means that Randy scored 3 standard deviations above the mean in math, which is the best performance among the four students. Therefore, the correct answer is A.

  References: Comparing with z-scores (video) | Z-scores | Khan Academy, 17 Important Data Visualization Techniques | HBS Online

• Question 73:

  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.

  Correct Answer: C

  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 74:

  Which of the following should be accomplished NEXT after understanding a business requirement for a data analysis report?

  A. Rephrase the business requirement.

  B. Determine the data necessary for the analysis

  C. Build a mock dashboard/presentation layout.

  D. Perform exploratory data analysis.

  Correct Answer: B

  Explanation: The next step after understanding a business requirement for a data analysis report is to determine the data necessary for the analysis. This step involves identifying the data sources, variables, metrics, and dimensions that are relevant and sufficient to answer the business question or problem. This step also involves assessing the availability, quality, and accessibility of the data, and planning how to collect, clean, and prepare the data for analysis. The other options are not the next steps after understanding a business requirement, but rather subsequent steps in the data analysis process. Rephrasing the business requirement is a step that can help clarify and refine the business question or problem before determining the data necessary for the analysis. Building a mock dashboard/presentation layout is a step that can help design and visualize the report before performing the data analysis. Performing exploratory data analysis is a step that can help explore and summarize the data before drawing conclusions and recommendations from the data. Reference: Data Analysis Process - DataCamp

• Question 75:

  An analyst needs to join two tables of data together for analysis. All the names and cities in the first table should be joined with the corresponding ages in the second table, if applicable.

  

  Which of the following is the correct join the analyst should complete. and how many total rows will be in one table?

  A. INNER JOIN, two rows

  B. LEFT JOIN. four rows

  C. RIGHT JOIN. five rows

  D. OUTER JOIN, seven rows

  Correct Answer: B

  Explanation: The correct join the analyst should complete is B. LEFT JOIN, four rows. A LEFT JOIN is a type of SQL join that returns all the rows from the left table, and the matched rows from the right table. If there is no match, the right table

  will have null values. A LEFT JOIN is useful when we want to preserve the data from the left table, even if there is no corresponding data in the right table1

  Using the example tables, a LEFT JOIN query would look like this:

  SELECT t1.Name, t1.City, t2.Age FROM Table1 t1 LEFT JOIN Table2 t2 ON t1.Name = t2.Name;

  The result of this query would be:

  Name City Age Jane Smith Detroit NULL John Smith Dallas 34 Candace Johnson Atlanta 45 Kyle Jacobs Chicago 39

  As you can see, the query returns four rows, one for each name in Table1. The name John Smith appears twice in Table2, but only one of them is matched with the name in Table1. The name Jane Smith does not appear in Table2, so the

  age column has a null value for that row.

• Question 76:

  A data analyst has a set with more than 40.000 rows in the sample schema below:

  

  The analyst would like to create one column that contains the customers' birth dates. Which of the following data quality dimensions would BEST explain the reason for compilation?

  A. Data accuracy

  B. Data completeness

  C. Data duplication

  D. Data integrity

  Correct Answer: D

  Explanation: Data integrity is the dimension that measures the consistency and validity of data across different data sources. In this case, the data analyst wants to create one column that contains the customers' birth dates, but the data is stored in different formats and locations in the sample schema. For example, some customers have their birth dates in the customer table, while others have their birth years in the sales table. To compile the data into one column, the data analyst needs to ensure that the data is consistent and valid across the tables. Therefore, data integrity is the best explanation for the reason for compilation. References: Data Quality Dimensions - DATAVERSITY, The 6 Data Quality Dimensions with Examples | Collibra

• Question 77:

  A data analyst has been asked to create a sales report that calculates the rolling 12-month average for sales. If the report will be published on November 1, 2020, which of the following months shouts the report cover?

  A. October 1, 2019 to October 31, 2020

  B. October 31, 2020 to November 1, 2021

  C. November 1, 2019 to October 31, 2020

  D. October 31, 2019 to October 31, 2020

  Correct Answer: A

  The report should cover the months from October 1, 2019 to October 31, 2020. A rolling 12-month average is a type of moving average that calculates the average of the last 12 months of data for each month. It is useful for smoothing out seasonal fluctuations and identifying long-term trends in the data. To calculate the rolling 12-month average for sales for November 1, 2020, the analyst needs to use the sales data from the previous 12 months, starting from November 1, 2019 and ending on October 31, 2020. The other options are either too short or too long to cover the required period.

• Question 78:

  Which of the following is an example of a discrete variable?

  A. The temperature of a hot tub

  B. The height of a horse

  C. The time to complete a task

  D. The number of people in an office

  Correct Answer: D

  Explanation: A discrete variable is a variable that can only take on a finite number of values, such as integers or categories. The number of people in an office is an example of a discrete variable, as it can only be a whole number. The temperature of a hot tub, the height of a horse, and the time to complete a task are examples of continuous variables, as they can take on any value within a range. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

• Question 79:

  A county in Illinois is conducting a survey to determine the mean annual income per household. The county is 427sq mi (2.65q km). Which of the following sampling methods would MOST likely result in a representative sample?

  A. A stratified phone survey of 100 people that is conducted between 2:00 p.m. and 3:00 p.m

  B. A systematic survey that is sent to 100 single-family homes in the county

  C. Surveys sent to ten randomly selected homes within 5mi (8km) of the county's office

  D. Surveys sent to 100 randomly selected homes that are reflective of the population

  Correct Answer: D

  Explanation: Surveys sent to 100 randomly selected homes that are reflective of the population. This is because a random sample is a type of sample that is selected by using a random method, such as a lottery or a computer-generated number, which ensures that every element in the population has an equal chance of being selected. A random sample can result in a representative sample, which means that the sample reflects the characteristics and diversity of the population. By sending surveys to 100 randomly selected homes that are reflective of the population, the analyst can ensure that the sample is representative of the county's households and their income levels. The other sampling methods are not likely to result in a representative sample. Here is why: A stratified phone survey of 100 people that is conducted between 2:00 p.m. and 3:00 p.m. would result in a biased sample, which means that the sample favors or excludes certain groups or elements in the population. By conducting the survey only between 2:00 p.m. and 3:00 p.m., the analyst would miss out on people who are not available or reachable at that time, such as those who are working or sleeping. This could affect the representativeness and generalizability of the sample. A systematic survey that is sent to 100 single-family homes in the county would result in an unrepresentative sample, which means that the sample does not reflect the characteristics and diversity of the population. By sending surveys only to single-family homes, the analyst would ignore other types of households, such as apartments, condos, or mobile homes. This could affect the accuracy and reliability of the sample. Surveys sent to ten randomly selected homes within 5mi (8km) of the county's office would result in a small sample, which means that the sample size is too low to capture the variability and diversity of the population. By sending surveys only to ten homes within a limited area, the analyst would miss out on many households that are located in different parts of the county. This could affect the precision and confidence of the sample.

• Question 80:

  A cereal manufacturer wants to determine whether the sugar content of its cereal has increased over the years. Which of the following is the appropriate descriptive statistic to use?

  A. Frequency

  B. Percent change

  C. Variance

  D. Mean

  Correct Answer: B

  This is because percent change is a type of descriptive statistic that measures the relative change or difference of a variable over time, such as the sugar content of cereal over years in this case. Percent change can be used to determine

  whether the sugar content of cereal has increased over years by comparing the initial and final values of the sugar content, as well as calculating the ratio or proportion of the change. For example, percent change can be used to determine

  whether the sugar content of cereal has increased over years by finding out how much more (or less) sugar there is in cereal now than before, as well as expressing it as a fraction or a percentage of the original sugar content. The other

  descriptive statistics are not appropriate to use to determine whether the sugar content of cereal has increased over years. Here is why:

  Frequency is a type of descriptive statistic that measures how often or how likely a value or an event occurs in a data set, such as how many times a certain sugar content appears in cereal in this case. Frequency does not measure the

  relative change or difference of a variable over time, but rather measures the occurrence or chance of a variable at a given time.

  Variance is a type of descriptive statistic that measures how much the values in a data set vary or deviate from the mean or average of the data set, such as how much variation there is in sugar content among different cereals in this case.

  Variance does not measure the relative change or difference of a variable over time, but rather measures the dispersion or spread of a variable at a given time. Mean is a type of descriptive statistic that measures the average value or central

  tendency of a data set, such as what is the typical sugar content of cereal in this case. Mean does not measure the relative change or difference of a variable over time, but rather measures the summary or representation of a variable at a

  given time.