CompTIA DA0-001 - CompTIA Data+ Certification Exam

TheChi-squared testis used to analyze relationships between twocategoricalvariables. In this case, age groups and voting preferences are both categorical variables, making chi-squared the most appropriate test​.

ETL is a process that is used during data integration to collect, blend, and load data. ETL stands for extract, transform, and load, which are the three main steps involved in moving data from different sources to a common destination, such as a data warehouse or a data lake. ETL helps to consolidate and standardize data for analysis and reporting purposes. References: CompTIA Data+ Certification Exam Objectives, page 12

When consolidating data from different sources, it is crucial to standardize field names to ensure consistency across datasets. This process involves aligning the field names so that they are the same in both tables, which simplifies the merging of data and subsequent analysis. Standardizing field names helps in maintaining data integrity and avoids confusion that may arise from having different names for the same data point.

Recode the data values (B) would not be necessary unless the data values themselves are inconsistent or in different formats. Overwriting the field names in one of the tables © could lead to loss of information or confusion. Editing the field names in the data dictionary (D) is helpful, but it does not address the immediate need to harmonize the field names in the actual datasets.

A confidence interval provides a range within which we expect a population parameter to lie, based on sample data. Increasing the confidence level means we want to be more certain that the interval contains the parameter, which affects the margin of error.

Option A:The margin of error does not change.

Rationale:This is incorrect. Increasing the confidence level requires a wider interval to maintain the higher degree of certainty, which increases the margin of error.

Option B:The interval remains the same.

Rationale:This is incorrect. To achieve a higher confidence level, the interval must widen, altering its range.

Option C:The interval becomes narrower.

Rationale:This is incorrect. A higher confidence level necessitates a wider interval to ensure the parameter is captured within the range.

Option D:The margin of error increases.

Rationale:Correct. Increasing the confidence level from 90% to 95% means we are more confident that the interval contains the true parameter. To achieve this increased confidence, the margin of error must increase, resulting in a wider interval.

 Scheduled report delivery is a feature that allows a data analyst to automate the generation and distribution of a report at a specified time and frequency. This would be the best method to ensure that the sales managers receive the weekly report on sales performance without manual intervention. Subscription access delivery is a feature that allows users to subscribe to a report and access it on demand, but it does not automate the delivery. Printing out a copy or uploading the report to the server are manual methods that require more time and effort from the data analyst. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA

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.

Handling missing datais crucial for maintaining the integrity of an analysis. Since the missing value belongs to anemployedindividual, the most appropriate method is toimpute the mean income of employed citizens.

Option A (Replace the missing value with the average of unemployed citizens):Incorrect. The missing income is for anemployedindividual, so it would be inappropriate to use the unemployed citizens' average.

Option B (Insert 0):Incorrect. Assigning 0 would be misleading since it does not reflect the income distribution for employed citizens.

Option C (Impute the mean of the other citizens' incomes):Correct.A common practice in data analytics ismean imputation, where missing values are replaced with the mean of similar cases (in this case, other employed citizens).

Option D (Exclude employed citizens from the analysis):Incorrect. This would remove valuable data and lead to biased results.