2026 Actual CompTIA DA0-001 Dumps, Updated Exam Questions for Guaranteed Pass

DA0-001 free sample questions and answers

CompTIA DA0-001 practice quiz with correct answers for search indexing.

1. Question 1

   Andy is a pricing analyst for a retailer. Using a hypothesis test, he wants to assess whether people who receive electronic coupons spend more on average.

   What should Andy's null hypothesis be?

   APeople who receive electronic coupons spend more on average.
   

   BPeople who receive electronic coupons spend less on average.
   

   CPeople who receive electronic coupons do not spend more on average.
   

   DPeople who do not receive electronic coupons spend more on average.
   

   Correct answer: C

   The null hypothesis presumes the status quo. Andy is testing whether or not people who receive an electronic coupon spend more on average, so, the null hypothesis states that people who receive the coupon do spend more on average.

   
   

2. Question 2

   Which of the following would be the best way to identify multicollinear attributes in a data set?

   ACorrelation coefficient
   

   BChi-squared test
   

   CTwo-sample f-test
   

   DTwo-way ANOVA
   

   Correct answer: A

   Multicollinearity in a dataset refers to the situation where two or more predictor variables are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy. In such cases, the correlation coefficient is a key statistical measure used to identify the presence of multicollinearity. It quantifies the degree to which two variables are linearly related.

   The Variance Inflation Factor (VIF) is another commonly used metric that is derived from the correlation coefficient. It assesses how much the variance of an estimated regression coefficient increases if your predictors are correlated. If no factors are correlated, the VIFs will all be equal to 1.

   While the other options listed---Chi-squared test, Two-sample f-test, and Two-way ANOVA---are valuable statistical tools, they serve different purposes and are not typically used to detect multicollinearity. The Chi-squared test is used for testing relationships between categorical variables, the Two-sample f-test compares variances across groups, and Two-way ANOVA is used to understand the interaction between two independent categorical variables on a continuous dependent variable.

   
   

   Multicollinearity in Regression Analysis: Problems, Detection, and Solutions1.

   What is multicollinearity and how to remove it?2.

   Detect and Treat Multicollinearity in Regression with Python3.

3. Question 3

   While reviewing survey data, an analyst notices respondents entered ''Jan,'' ''January,'' and ''01'' as responses for the month of January. Which of the following steps should be taken to ensure data consistency?

   ADelete any of the responses that do not have ''January'' written out.
   

   BReplace any of the responses that have ''01''.
   

   CFilter on any of the responses that do not say ''January'' and update them to ''January''.
   

   DSort any of the responses that say ''Jan'' and update them to ''01''.
   

   Correct answer: C

   Filter on any of the responses that do not say ''January'' and update them to ''January''. This is because filtering and updating are data cleansing techniques that can be used to ensure data consistency, which means that the data is uniform and follows a standard format. By filtering on any of the responses that do not say ''January'' and updating them to ''January'', the analyst can make sure that all the responses for the month of January are written in the same way. The other steps arenot appropriate for ensuring data consistency. Here is why:

   Deleting any of the responses that do not have ''January'' written out would result in data loss, which means that some information would be missing from the data set. This could affect the accuracy and reliability of the analysis.

   Replacing any of the responses that have ''01'' would not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: ''Jan'' and ''January''. This could cause confusion and errors in the analysis.

   Sorting any of the responses that say ''Jan'' and updating them to ''01'' would also not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: ''01'' and ''January''. This could also cause confusion and errors in the analysis.

   
   

4. Question 4

   Which of the following describes the method of sampling in which elements of data are selected randomly from each of the small subgroups within a population?

   ASimple random
   

   BCluster
   

   CSystematic
   

   DStratified
   

   Correct answer: D

   This is because stratified is a type of sampling in which elements of data are selected randomly from each of the small subgroups within a population, such as age groups, gender groups, or income groups. Stratified sampling can be used to ensure that the sample is representative and proportional of the population, as well as reduce the sampling error or bias. For example, stratified sampling can be used to select a sample of voters from different political parties based on their proportion in the population. The other types of sampling are not the types of sampling in which elements of data are selected randomly from each of the small subgroups within a population. Here is why:

   Simple random is a type of sampling in which elements of data are selected randomly from the entire population, without dividing it into any subgroups. Simple random sampling can be used to ensure that every element in the population has an equal chance of being selected, as well as avoid any systematic error or bias. For example, simple random sampling can be used to select a sample of students from a school by using a lottery or a computer-generated number.

   Cluster is a type of sampling in which elements of data are selected randomly from a few large subgroups within a population, such as regions, districts, or schools. Cluster sampling can be used to reduce the cost and complexity of sampling, as well as increase the feasibility and convenience of sampling. For example, cluster sampling can be used to select a sample of households from a few neighborhoods by using a map or a list.

   Systematic is a type of sampling in which elements of data are selected at regular intervals from an ordered list or sequence within a population, such as every nth element or every kth element. Systematic sampling can be used to simplify and speed up the sampling process, as well as ensure that the sample covers the entire range or scope of the population. For example, systematic sampling can be used to select a sample of books from a library by using an alphabetical order or a numerical order.

   
   

5. Question 5

   Which of the following data manipulation techniques is an example of a logical function?

   AWHERE
   

   BAGGREGATE
   

   CBOOLEAN
   

   DIF
   

   Correct answer: D

   This is because an IF function is a type of logical function that returns a value based on a condition or a set of conditions. An IF function can be used to manipulate data by applying different actions or calculations depending on whether the condition is true or false. For example, an IF function in Excel that can achieve this is:

   =IF (condition, value_if_true, value_if_false)

   The other data manipulation techniques are not examples of logical functions. Here is why:

   WHERE is a type of clause that filters data based on a condition or a set of conditions. A WHERE clause can be used to manipulate data by selecting only the rows that satisfy the condition(s). For example, a WHERE clause in SQL that can achieve this is:

   

   AGGREGATE is a type of function that performs a calculation on a group of values, such as sum, average, count, etc. An AGGREGATE function can be used to manipulate data by summarizing or aggregating the values in a column or a table. For example, an AGGREGATE function in SQL that can achieve this is:

   

   BOOLEAN is a type of data type that represents two possible values: true or false. A BOOLEAN data type can be used to manipulate data by storing or returning logical values based on a condition or a set of conditions. For example, a BOOLEAN data type in Python that can achieve this is: