CompTIA DA0-001 Practice Test - Questions Answers, Page 20

Missing data is a type of data quality issue that occurs when some values in a data set are not recorded or available. Missing data can affect the validity and reliability of survey results, especially if the missing values are not random or ignorable. Missing data can also reduce the sample size and the statistical power of the analysis12

If 100% participation is needed on survey results, a data analyst would look for missing data first, because missing data would indicate that some participants did not complete or submit the survey, or that some responses were not recorded or transmitted correctly. A data analyst would need to identify the causes and patterns of missing data, and apply appropriate methods to handle or prevent missing data, such as imputation, deletion, weighting, or follow-up12

The correct answer is B. Recoding.

Recoding is a data manipulation technique that involves changing the values or categories of a variable to make it more suitable for analysis. Recoding can be used to simplify or group the data, to correct errors or inconsistencies, or to create new variables from existing ones12

In the example, the analyst used recoding to change the values of Var001, Var002, Var003, and Var004 from numerical to textual form. The analyst also used recoding to assign meaningful labels to the values, such as "Absent" for 0, "Present" for 1, "Low" for 2, "Medium" for 3, and "High" for 4.

This makes the data more understandable and easier to analyze.

Correcting the dates so they have the same format is the most important thing for an analyst to do when transforming the table for a trend analysis. Trend analysis is a method of analyzing data over time to identify patterns, changes, or relationships. To perform a trend analysis, the data needs to have a consistent and comparable format, especially for the date or time variables.

In the example, the date purchased column has two different formats: YYYY-MM-DD and MM/DD/YYYY. This could cause errors or confusion when sorting, filtering, or plotting the data over time. Therefore, the analyst should correct the dates so they have the same format, such as YYYYMM-DD, which is a standard and unambiguous format.

The best sampling method for the data analyst's need is C. Stratified sampling.

Stratified sampling is a type of probability sampling that involves dividing the population into homogeneous groups or strata based on some characteristic, such as state, and then randomly selecting a proportional number of individuals from each stratum. Stratified sampling ensures that every group is adequately represented in the sample, and reduces the sampling error and variability12

Systematic sampling is not correct, because it involves selecting every nth individual from the population, starting from a random point. Systematic sampling does not guarantee that every state will have a similar proportion of data in the sample, and may introduce bias or error if there is a hidden pattern or order in the population12

Convenience sampling is not correct, because it involves selecting individuals who are easily accessible or available to the researcher. Convenience sampling is a type of non-probability sampling that does not involve random selection, and may result in a biased or unrepresentative sample12 Random sampling is not correct, because it involves selecting individuals from the population at random, without any grouping or stratification. Random sampling may not produce a sample that has a similar proportion of data from every state, especially if the population is large or heterogeneous.

Random sampling may also have a higher sampling error and variability than stratified sampling12

Two reasons to conduct data cleansing are:

To improve accuracy: Data cleansing helps to ensure that the data is correct, consistent, and reliable.

This can improve the quality and validity of the analysis, as well as the decision-making and outcomes based on the data12

To calculate trends: Data cleansing helps to remove or resolve any errors, outliers, or missing values that could distort or skew the dat a. This can help to identify and measure the patterns, changes, or relationships in the data over time13

The correct answer is D. $702,500.

To find the price of the Rose unit, we need to use the average cost per square foot of the original floor plans. The average cost per square foot is calculated by dividing the price by the square footage of each unit type. Using the data from the table, we can do the following:

Jasmine: $345,000 / 1,000 = $345 per square foot Orchid: $525,000 / 2,000 = $262.5 per square foot Azalea: $375,000 / 1,500 = $250 per square foot Tulip: $450,000 / 1,800 = $250 per square foot

The average cost per square foot of the original floor plans is the mean of these four values, which is ($345 + $262.5 + $250 + $250) / 4 = $276.875 per square foot.

To find the price of the Rose unit, we need to multiply the average cost per square foot by the square footage of the Rose unit. The Rose unit has a square footage of 2,535, according to the table.

Therefore, the price of the Rose unit is $276.875 x 2,535 = $702,421.875.

Rounding to the nearest whole number, we get $702,500 as the price of the Rose unit.

The best answer is

A) JSON is quicker to read and write.

JSON (JavaScript Object Notation) is a lightweight data-interchange format that is based on the JavaScript programming language and easy to understand and generate. JSON uses a simple syntax that consists of name-value pairs and arrays, and does not require any end tags or attributes. JSON is quicker to read and write than XML (Extensible Markup Language), which is a markup language that uses a tag structure to represent data items. XML has a more complex and verbose syntax that requires end tags, attributes, and namespaces123

The best answer is C. Microsoft Power BI.

Microsoft Power BI is a business analytics and business intelligence service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. Power BI can connect to multiple data sources, clean and transform data, create custom calculations, and visualize data through charts, graphs, and tables. Power BI can be accessed through a web browser, mobile device, or desktop application and integrated with other Microsoft tools like Excel and SharePoint12 Python is not correct, because Python is a general-purpose programming language that can be used for various applications, including data analysis and visualization. However, Python is not a dedicated business analytics tool, and it requires coding or programming skills to create reports and dashboards.

R is not correct, because R is a programming language and software environment for statistical computing and graphics. R can be used for data analysis and visualization, but it is not a specialized business analytics tool, and it requires coding or programming skills to create reports and dashboards.

SAS is not correct, because SAS is a software suite for advanced analytics, business intelligence, data management, and predictive analytics. SAS can provide interactive visualizations and business capabilities, but it does not have an interface that is simple enough for end users to create their own reports and dashboards. SAS also requires coding or programming skills to use its features.