CompTIA CompTIA Certifications DA0-001 Questions & Answers

• Question 91:

  The current date is July 14, 2020. A data analyst has been asked to create a report that shows the company's year-over-year Q2 2020 sales. Which of the following reports should the analyst compare?

  A. A Q2 2020 and Q4 2019

  B. YTD 2020 and YTD 2019

  C. Q2 2020 and Q2 2019

  D. Q2 2020 and Q2 2021

  Correct Answer: C

  Explanation: To create a report that shows the company's year-over-year Q2 2020 sales, the analyst should compare the sales data from Q2 2020 and Q2 2019. Year-over-year (YoY) analysis is a method of comparing the performance of a business or a financial instrument over the same period in different years. It helps to identify trends, growth patterns, and seasonal fluctuations. Q2 refers to the second quarter of a year, which is usually from April to June. Therefore, the correct answer is C. References: YoY - Year over Year Analysis - Definition, and Examples, What is an Annual Sales Report: Definition, metrics, and tips - Snov.io

• Question 92:

  Which of the following roles is responsible for ensuring an organization's data quality, security, privacy, and regulatory compliance?

  A. Data owner.

  B. Data steward.

  C. Data custodian.

  D. Data processor.

  Correct Answer: B

  Correct answer B. Data steward.

  A data steward is responsible for leading an organization's data governance activities, which include data quality, security, privacy, and regulatory compliance.

• Question 93:

  Given the table below: Which of the following variable types BEST describes the "Year" column?

  

  A. Numeric

  B. Date

  C. Alphanumeric

  D. Text

  Correct Answer: B

  Explanation: This is because date is a type of variable that represents a specific point or period in time, such as a day, a month, or a year. Date variables can be used to store, manipulate, or analyze temporal data, such as transaction dates,

  birth dates, or expiration dates. For example, date variables can be used to calculate the duration or the difference between two dates, or to filter or sort the data by date. The other variable types are not correct descriptions of the "Year"

  column. Here is why:

  Numeric is a type of variable that represents a numerical value, such as an integer, a decimal, or a fraction. Numeric variables can be used to store, manipulate, or analyze quantitative data, such as amounts, prices, or scores. For example,

  numeric variables can be used to perform arithmetic operations or calculations on the data, or to measure the central tendency or the dispersion of the data.

  Alphanumeric is a type of variable that represents a combination of alphabetic and numeric characters, such as letters, numbers, symbols, or spaces. Alphanumeric variables can be used to store, manipulate, or analyze textual data, such as

  names, addresses, or codes. For example, alphanumeric variables can be used to concatenate or split the data, or to search or match the data using patterns or expressions.

  Text is a type of variable that represents a sequence of alphabetic characters, such as letters or words. Text variables can be used to store, manipulate, or analyze textual data, such as names, categories, or labels. For example, text

  variables can be used to change the case or the length of the data, or to compare or classify the data using criteria or rules.

• Question 94:

  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

  Correct Answer: B

  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 95:

  Which of the following is a non-parametric test?

  A. One-sample t-test

  B. Two-way ANOVA

  C. Correlation coefficient

  D. Spearman's rank correlation

  Correct Answer: D

  The correct answer is D. Spearman's rank correlation. Spearman's rank correlation is a non-parametric test that measures the strength and direction of the relationship between two variables that are ranked (ordinal) or continuous. Spearman's rank correlation does not assume that the data follows a normal distribution or that the variables are linearly related. Spearman's rank correlation is based on the ranks of the data rather than the actual values12

  A. One-sample t-test is not correct, because it is a parametric test that compares the mean of a sample to a specified value. One-sample t-test assumes that the data follows a normal distribution and has a known population standard deviation34 B. Two-way ANOVA is not correct, because it is a parametric test that compares the means of two or more groups that are influenced by two independent factors. Two-way ANOVA assumes that the data follows a normal distribution, has homogeneous variances, and has independent observations.

  C. Correlation coefficient is not correct, because it is a parametric test that measures the strength and direction of the linear relationship between two continuous variables. Correlation coefficient assumes that the data follows a bivariate normal distribution and has no outliers.

• Question 96:

  The ACME Corporation hired an analyst to detect data quality issues in their Excel documents. Which of the following are the most common issues? (Select TWO)

  A. Apostrophe.

  B. Commas.

  C. Symbols.

  D. Duplicates.

  E. Misspellings.

  Correct Answer: DE

  1.

  Duplicates

  2.

  Misspellings

  The most common data quality issues are difficult to resolve in Excel because of their rigidity. It forces analysts to do a ton of manual work, which results in a high probability of an error being introduced to the data set. Those common issues

  include:

  -Blanks

  -Nulls

  -Outliers

  -Duplicates

  -Extra spaces

  -Misspellings

  -Abbreviations and domain-specific variations

  -Formula error codes

  When introduced, these errors can skew or even invalidate the resulting analysis. A smart tool would minimize the possibility of error by automating the manual work. In Excel, you might look for data quality issues in one of two ways. First,

  you might use auto filters on specific columns to scan for anomalies and blanks or you might use a pivot table to find gaps and discrepancies.

  In either case, you're scanning for the anomalies yourself. Suffice it to say that's not a very efficient process. It also means accuracy is only as good as the analyst's eye, so the probability of error varies throughout the day.

• Question 97:

  Which one the following is not considered an aggregate function?

  A. SUM

  B. MIN

  C. SELECT

  D. MAX

  Correct Answer: C

  Explanation: The option that is not considered an aggregate function is SELECT. An aggregate function is a function that performs a calculation on a set of values and returns a single value. Examples of aggregate functions are SUM, MIN, MAX, AVG, COUNT, etc. SELECT is not an aggregate function, but a SQL command that is used to select data from a table or a query. Reference: SQL Aggregate Functions - W3Schools

• Question 98:

  A sales analyst needs to report how the sales team is performing to target. Which of the following files will be important in determining 2019 performance attainment?

  A. 2018 goal data

  B. 2018 actual revenue

  C. 2019 goal data

  D. 2019 commission plan

  Correct Answer: C

  Answer: C. 2019 goal data To report how the sales team is performing to target, the sales analyst needs to compare the actual sales revenue with the expected or planned sales revenue for the same period. The 2019 goal data is the file that contains the expected or planned sales revenue for the year 2019, which is the target that the sales team is aiming to achieve. By comparing the 2019 goal data with the 2019 actual revenue, the sales analyst can calculate the performance attainment, which is the percentage of the goal that was met by the sales team. Option A is incorrect, as 2018 goal data is not relevant for determining 2019 performance attainment. The 2018 goal data contains the expected or planned sales revenue for the year 2018, which is not the target that the sales team is aiming to achieve in 2019. Option B is incorrect, as 2018 actual revenue is not relevant for determining 2019 performance attainment. The 2018 actual revenue contains the actual sales revenue for the year 2018, which is not comparable with the 2019 goal data or the 2019 actual revenue. Option D is incorrect, as 2019 commission plan is not relevant for determining 2019 performance attainment. The 2019 commission plan contains the rules and rates for calculating and paying commissions to the sales team based on their performance attainment, but it does not contain the expected or planned sales revenue for the year 2019.

• Question 99:

  A user imports a data file into the accounts payable system each day. On a regular basis. the field input is not what the system is expecting. so it results in an error for the row and a broken import process. To resolve the issue, the user opens the file, finds the error in the row, and manually corrects it before attempting the import again. The import sometimes breaks on subsequent attempts. though. Which of the following changes should be made to this process to reduce the number of errors?

  A. Delete all incorrect inputs and upload the corrected file.

  B. Have the user manually review the file for data completeness before loading it

  C. Create a data field to data type validator to run the file through prior to import.

  D. Spot-check the file prior to import to catch and correct field errors.

  Correct Answer: C

  Explanation: A data field to data type validator is a tool or a process that checks if the data in each field of a file matches the expected data type, such as text, number, date, etc. A data field to data type validator can help to identify and correct any errors or inconsistencies in the data before importing it into the accounts payable system. This would reduce the number of errors and broken imports, as well as save time and effort for the user.

• Question 100:

  Given the following report:

  

  Which of the following components need to be added to ensure the report is point-in-time and static? (Choose two.)

  A. A control group for the phrases

  B. A summary of the KPIs

  C. Filter buttons for the status

  D. The date when the report was last accessed

  E. The time period the report covers

  F. The date on which the report was run

  Correct Answer: E

  The date on which the report was run. This is because the time period the report covers and the date on which the report was run are two components that need to be added to ensure the report is point-in-time and static, which means that the report shows the data as it was at a specific moment or interval in time, and does not change or update with new data. By adding the time period the report covers and the date on which the report was run, the analyst can indicate when and for how long the data was collected and analyzed, as well as avoid any confusion or ambiguity about the currency or validity of the data. The other components do not need to be added to ensure the report is point-in-time and static. Here is why:

  A control group for the phrases is a type of group that serves as a baseline or a reference for comparison with another group that is exposed to some treatment or intervention, such as a target phrase in this case. A control group for the phrases does not need to be added to ensure the report is point-in-time and static, because it does not affect the time frame or the stability of the data. However, a control group for the phrases could be useful for evaluating the effectiveness or impact of the target phrases on customer satisfaction or retention. A summary of the KPIs is a type of document that provides an overview or a highlight of the key performance indicators (KPIs), which are measurable values that indicate how well an organization or a process is achieving its goals or objectives. A summary of the KPIs does not need to be added to ensure the report is point-in-time and static, because it does not affect the time frame or the stability of the data. However, a summary of the KPIs could be useful for communicating or presenting the main findings or insights from the report. Filter buttons for the status are a type of feature or function that allows users to select or deselect certain values or categories in a column or a table, such as ticket statuses in this case. Filter buttons for the status do not need to be added to ensure the report is point-in- time and static, because they do not affect the time frame or the stability of the data. However, filter buttons for the status could be useful for exploring or analyzing different aspects or segments of the data.