Question # 1
| A data analyst is creating a report that will provide information about various regions, products, and time periods. Which of the following formats would be the most efficient way to deliver this report? | | A. A workbook with multiple tabs for each region
| | B. A daily email with snapshots of regional summaries
| | C. A static report with a different page for every filtered view
| | D. A dashboard with filters at the top that the user can toggle
|
D. A dashboard with filters at the top that the user can toggle
Explanation:
The best format to deliver this report is D. A dashboard with filters at the top that the user can toggle.
A dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance1 A dashboard with filters at the top that the user can toggle would allow the user to easily and quickly access the information they need about various regions, products, and time periods, without having to navigate through multiple tabs, pages, or emails. A dashboard with filters would also enable the user to compare and contrast different views of the data and see how they change over time. A dashboard with filters would also be more interactive and engaging than a static or email report.
A workbook with multiple tabs for each region would not be an efficient way to deliver this report, because it would require the user to switch between different tabs to see the information they need. This would make it harder to compare and contrast different regions, products, and time periods, and also increase the risk of errors or confusion. A workbook with multiple tabs would also be less visually appealing and more cluttered than a dashboard.
A daily email with snapshots of regional summaries would not be an efficient way to deliver this report, because it would limit the user’s ability to explore the data in depth and customize their view. A daily email would also be dependent on the frequency and timing of the email delivery, which might not match the user’s needs or preferences. A daily email would also be more likely to be ignored or deleted than a dashboard that is always accessible.
A static report with a different page for every filtered view would not be an efficient way to deliver this report, because it would create a very long and cumbersome report that would be difficult to read and understand. A static report would also not allow the user to change or update the filters as they wish, or see how the data changes over time. A static report would also be less interactive and engaging than a dashboard.
Question # 2
| A data analyst is compiling a report that a Chief Executive Officer needs for an impromptu meeting. The report should include information on the previous day's performance. Which of the following reports should the analyst provide? | | A. Tactical | | B. Ad hoc
| | C. Dynamic
| | D. Recurring |
B. Ad hoc
Question # 3
| Which one of the following values will appear first if they are sorted in descending order?
| | A. Aaron | | B. Molly | | C. Xavier | | D. Adam |
C. Xavier
Explanation:
The value that will appear first if they are sorted in descending order is Xavier. Descending order means arranging values from the largest to the smallest, or from the last to the first in alphabetical order. In this case, Xavier is the last name in alphabetical order, so it will appear first when sorted in descending order. The other names will appear in the following order: Molly, Adam, Aaron.
Question # 4
| You are working with a dataset and need to swap the values in rows with those in columns.
What action do you need to perform? | | A. Recording | | B. Filtering | | C. Aggregation | | D. Transposition |
D. Transposition
Explanation:
Transpose creates a new data file in which the rows and columns in the original data file are transposed so that cases (rows) become variables and variables (columns) become cases. Transpose automatically creates new variable names and displays a list of the new variable names.
Transposing data is useful for data analysis. At times, we have to pull data from various files with different formats for analysis and preparing reports. In such circumstances, we may have to transpose some data from one file to the other. In excel, we can transpose data in multiple ways.
Question # 5
| 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.
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.
Question # 6
| 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.
|
C. Rescale the data.
Explanation:
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°F, the rescaled value in Celsius is:
Celsius = (68 - 32) * 5/9 Celsius = 20°C
Rescaling the data can help the analyst to report the temperatures in a consistent and accurate way, and to avoid any confusion or errors that may arise from using different scales. Rescaling can also make the data more comparable and compatible with other data sources or standards that use the same scale12.
Question # 7
| Which of the following is an example of a discrete data type?
| | A. 8in (20cm)
| | B. 5 kids
| | C. 2.5mi (4km)
| | D. 10.7lbs (4.9kg)
|
B. 5 kids
Explanation:
A discrete data type is a data type that can only take on a finite number of values, such as integers or categories. An example of a discrete data type is the number of kids, as it can only be a whole number. The other options are examples of continuous data types, as they can take on any value within a range. The length in inches or centimeters, the distance in miles or kilometers, and the weight in pounds or kilograms are all continuous data types.
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