CompTIA DA0-001 Online Practice Questions and Exam Preparation

CompTIA DA0-001 Online Questions & Answers

• Question 71:

  An analyst in a consumer bank department wants to showcase the concentration of accounts opened in the United States by ZIP Code to describe the effectiveness of the bank's marketing campaigns.

  Which of the following would be the best way to visualize the data?

  A. A stacked chart
  B. A tree map
  C. A waterfall chart
  D. A geographic map
  D. A geographic map

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  Explanation

• Question 72:

  An e-commerce company recently tested a new website layout. The website was tested by a test group of customers, and an old website was presented to a control group. The table below shows the percentage of users in each group who made purchases on the websites:

  

  Which of the following conclusions is accurate at a 95% confidence interval?

  A. In Germany, the increase in conversion from the new layout was not significant.
  B. In France, the increase in conversion from the new layout was not significant.
  C. In general, users who visit the new website are more likely to make a purchase.
  D. The new layout has the lowest conversion rates in the United Kingdom.
  A. In Germany, the increase in conversion from the new layout was not significant.

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  Explanation

  The p-value is a measure of how likely it is to observe a difference in conversion rates as large or larger than the one observed, assuming that there is no difference between the groups. A common threshold for statistical significance is 0.05,

  meaning that there is a 5% or less chance of observing such a difference by chance alone. The table shows the p-values for each country, and we can see that only Germany has a p- value above 0.05 (0.13). This means that we cannot

  reject the null hypothesis that there is no difference in conversion rates between the test and control groups in Germany. Therefore, the increase in conversion from the new layout was not significant in Germany. For the other countries, the p-

  values are below 0.05, indicating that the increase in conversion from the new layout was statistically significant. Option A is correct. Option B is incorrect because the increase in conversion from the new layout was significant in France (pvalue = 0.002).

  Option C is incorrect because it does not account for the variation across countries. While the overall conversion rate for the test group (8.4%) is higher than the control group (6.8%), this difference may not be statistically significant when we

  consider the country-specific effects.

  Option D is incorrect because the new layout has the highest conversion rate in the United Kingdom (9.6%), not the lowest.

  References:

  P-value Calculator and Statistical Significance Calculator p-value Calculator | Formula | Interpretation

  How to obtain the P value from a confidence interval | The BMJ Confidence Intervals and P-values for Percent Change / Relative Difference

• Question 73:

  An analyst runs a report on a daily basis, and the number of datapoints must be validated before the data can be analyzed. The number of datapoints increases each day by approximately 20% of the total number from the day before. On a given day, the number of datapoints was 8,798.

  Which of the following should be the total number of datapoints on the next day?

  A. 7,038
  B. 9,600
  C. 10,600
  D. 10,800
  C. 10,600

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  Explanation

  This is because the number of datapoints increases each day by approximately 20% of the total number from the day before. Therefore, to find the number of datapoints on the next day, we can use the formula:

  

  Plugging in the given values, we get:

  

  Since we are dealing with whole numbers, we can round up the result to the nearest integer, which is 10,600.

• Question 74:

  An analyst has been asked to validate data quality. Which of the following are the BEST reasons to validate data for quality control purposes? (Choose two.)

  A. Retention
  B. Integrity
  C. Transmission
  D. Consistency
  E. Encryption
  F. Deletion
  B. Integrity

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  Explanation

  Integrity and

  D. Consistency. This is because integrity and consistency are two of the best reasons to validate data for quality control purposes, which means to check and ensure that the data is accurate, complete, reliable, and usable for the intended analysis or purpose. By validating data for integrity and consistency, the analyst can prevent or correct any errors or issues in the data that could affect the validity or reliability of the analysis or the results. Here is what integrity and consistency mean in terms of data quality:

  Integrity refers to the completeness and validity of the data, which means that the data has no missing, incomplete, or invalid values that could compromise its meaning or usefulness. For example, validating data for integrity could involve checking for null values, outliers, or incorrect data types in the data set. Consistency refers to the uniformity and standardization of the data, which means that the data follows a common format, structure, or rule across different sources or systems. For example, validating data for consistency could involve checking for spelling, punctuation, or capitalization errors in the data set. The other reasons are not the best reasons to validate data for quality control purposes. Here is why:

  Retention refers to the storage and preservation of the data, which means that the data is kept and maintained in a secure and accessible way for future use or reference. Retention does not need to be validated for quality control purposes, because it does not affect the accuracy or reliability of the data itself. Transmission refers to the transfer and exchange of the data, which means that the data is moved or shared between different sources or systems in a fast and efficient way. Transmission does not need to be validated for quality control purposes, because it does not affect the completeness or validity of the data itself. Encryption refers to the protection and security of the data, which means that the data is encoded or scrambled in a way that prevents unauthorized access or use. Encryption does not need to be validated for quality control purposes, because it does not affect the uniformity or standardization of the data itself. Deletion refers to the removal and disposal of the data, which means that the data is erased or destroyed in a way that prevents recovery or retrieval. Deletion does not need to be validated for quality control purposes, because it does not affect the meaning or usefulness of the data itself.

• Question 75:

  A company's human resources department has asked a data analyst to categorize the income of all employees into five salary bands: Which of the following types of functions would be the most appropriate to use?

  

  A. Statistical
  B. Aggregate
  C. Logical
  D. Mathematical
  C. Logical

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  Explanation

  Short Logical functions are the most appropriate to use for categorizing data into bands, because they allow the data analyst to apply conditional statements and criteria to the data values. For example, the IF function can be used to assign a band name based on whether a value meets a certain condition or not. Other logical functions that can be useful for categorizing data are AND, OR, NOT, and IFERROR.

• Question 76:

  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.

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  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 77:

  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.
  D. DML.

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  Explanation

  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.

• Question 78:

  A data analyst is performing a data merge within a spreadsheet using the tables below: https://www.bing.com/images/blob?bcid=S1XCF9p02M4GjpbGxHj0lrIaj9sw.....4c

  

  The analyst is attempting to pull the addresses from Table 2 into Table 1 using the last names and is receiving an error message.

  Which of the following steps can the analyst perform to fix the error?

  A. Use concatenate to combine the tables.
  B. Ensure the formula is pulling from right to left.
  C. Sort the data by the last name field.
  D. Review the spelling and data type.
  D. Review the spelling and data type.

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  Explanation

  The error in merging data from Table 2 into Table 1 using last names could be due to discrepancies in spelling or data type between the two tables. It is essential to ensure that the last names are spelled consistently and that the data types

  are compatible for a successful merge. Option D suggests reviewing these aspects, which can potentially resolve the error, ensuring that each last name in Table 1 accurately corresponds to the same last name in Table 2, allowing for a

  successful data pull of addresses.

  References:

  This answer is based on general data analytics practices and does not reference a specific document.

• Question 79:

  A user receives a large custom report to track company sales across various date ranges. The user then completes a series of manual calculations for each date range.

  Which of the following should an analyst suggest so the user has a dynamic, seamless experience?

  A. Create multiple reports, one for each needed date range.
  B. Build calculations into the report so they are done automatically.
  C. Add macros to the report to speed up the filtering and calculations process.
  D. Create a dashboard with a date range picker and calculations built in.
  D. Create a dashboard with a date range picker and calculations built in.

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  Explanation

  Create a dashboard with a date range picker and calculations built in. This is because a dashboard is a type of visualization that displays multiple charts or graphs on a single page, usually to provide an overview or summary of some data or information. A dashboard can be used to track company sales across various date ranges by showing different metrics and indicators related to sales, such as revenue, volume, or growth. By creating a dashboard with a date range picker and calculations built in, the analyst can suggest a way for the user to have a dynamic, seamless experience, which means that the user can interact with and customize the dashboard according to their needs or preferences, as well as avoid any manual work or errors. For example, a date range picker is a type of feature or function that allows users to select or adjust the time period for which they want to see the data on the dashboard, such as daily, weekly, monthly, or quarterly. A date range picker can make the dashboard dynamic, as it can automatically update or refresh the dashboard with new data based on the selected time period. Calculations are mathematical operations or expressions that can be performed on the data on the dashboard, such as addition, subtraction, multiplication, division, average, sum, etc. Calculations can make the dashboard seamless, as they can eliminate the need for manual calculations for each date range, as well as ensure accuracy and consistency of the results. The other ways are not the best ways to provide a dynamic, seamless experience for the user. Here is why:

  Creating multiple reports, one for each needed date range would not provide a dynamic, seamless experience for the user, but rather create a static, cumbersome experience, which means that the user cannot interact with or customize the reports according to their needs or preferences, as well as have to deal with multiple files or pages. For example, creating multiple reports would make it difficult for the user to compare or contrast the sales across different date ranges, as well as increase the workload and complexity of managing and maintaining the reports. Building calculations into the report so they are done automatically would not provide a dynamic, seamless experience for the user, but rather provide a partial, limited experience, which means that the user can only benefit from one aspect or feature of the report, but not from others. For example, building calculations into the report would help with avoiding manual work or errors, but it would not help with interacting with or customizing the report according to different date ranges. Adding macros to the report to speed up the filtering and calculations process would not provide a dynamic, seamless experience for the user, but rather provide an advanced, complex experience, which means that the user would need to have some technical skills or knowledge to use or apply the macros, as well as face some potential risks or challenges. For example, adding macros to the report would require the user to know how to write or run the macros, which are a type of code or script that automates certain tasks or actions on the report, such as filtering or calculating the data. Adding macros to the report could also expose the user to some security or compatibility issues, such as viruses, malware, or errors.

• Question 80:

  A data analyst has been asked to merge the tables below, first performing an INNER JOIN and then a LEFT JOIN: Customer Table In-store Transactions

  

  

  Which of the following describes the number of rows of data that can be expected after performing both joins in the order stated, considering the customer table as the main table?

  A. INNER: 6 rows; LEFT: 9 rows
  B. INNER: 9 rows; LEFT: 6 rows
  C. INNER: 9 rows; LEFT: 15 rows
  D. INNER: 15 rows; LEFT: 9 rows
  C. INNER: 9 rows; LEFT: 15 rows

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  Explanation

  An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is

  the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.

  To perform an INNER JOIN, we can use the following SQL query:

  SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

  This query will return 9 rows of data, as shown below:

  customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M | Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3 | 2000 | 2020-01-03 2

  | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 | 4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08

  7|MILA|MORRIS|F|N|9|8000|2020-01-09 To perform a LEFT JOIN, we can use the following SQL query:

  SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

  This query will return 15 rows of data, as shown below:

  customer_id|name|lastname|gender|marital_status|transaction_id|amount|date 1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02 2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|202001-04 3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06 5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08 7||MILA||MORRIS||F||N||9||8000||2020-01-09 8||JENNY||

  DWARTH||F||Y||NULL||NULL||NULL

  As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.

  Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.

  SQL Joins - W3Schools