CompTIA DA0-001 Practice Test - Questions Answers, Page 21

Answer C) While Strategy 2 does not result in the highest sales of Product D. over

Explanation:

all products it appears to be the most effective.

A summary statement that upholds integrity in data reporting should be accurate, unbiased, and supported by evidence. Option C is the only statement that meets these criteria, as it reflects the data shown in the bar graph without exaggerating or distorting it. Option C also acknowledges the limitation of the statement by using the word "appears", which indicates that there may be other factors or variables that affect the sales performance.

Option A is inaccurate, as sales are not approximately equal for Product A and Product B across all strategies. Product A has higher sales than Product B in strategies 1, 3, and 5, while Product B has higher sales than Product A in strategies 2 and 4.

Option B is biased, as it does not consider the sales of different products in each strategy. Strategy 4 provides the best sales for Product B, but not for the other products. Strategy 5 has the highest total sales across all products, as shown by the black line graph.

Option D is unsupported by evidence, as it does not explain why Product D should be promoted more than the other products in all strategies. Product D has the lowest sales among all products in strategies 1, 3, and 4, and only slightly higher sales than Product C in strategies 2 and 5.

Answer C) Review the business questions to understand the scope.

Explanation:

The first step an analyst should perform upon receiving a business request for analysis is to review the business questions to understand the scope of the problem, the objectives, and the expected outcomes. This will help the analyst to define the analytical approach, identify the data needs and sources, and plan the analysis process. Reviewing the business questions will also help the analyst to communicate with the stakeholders and clarify any assumptions or ambiguities1.

Option A is incorrect, as determining the data needs and sources for analysis is not the first step, but rather a subsequent step that depends on the business questions and the analytical approach.

Option B is incorrect, as initiating the analysis for exploratory data analysis is not the first step, but rather a part of the analysis process that involves examining and summarizing the data, identifying patterns and outliers, and testing hypotheses.

Option D is incorrect, as finalizing the methodology to solve the problem is not the first step, but rather a later step that involves selecting and applying the appropriate analytical techniques, tools, and models to answer the business questions.

Answer B) Ordinary least squares

Explanation:

Ordinary least squares (OLS) is a type of linear regression that is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable.

Use when: The relationship between the predictor variable(s) and the response variable is reasonably linear. The response variable is a continuous numeric variable1.

In this case, the data analyst is interested in understanding how ice cream consumption (the response variable) is affected by different attributes, such as cost, temperature, and income level (the predictor variables). Assuming that these variables have a linear relationship, OLS can be used to estimate the coefficients of the regression equation that best fits the dat a. OLS can also provide measures of goodness-of-fit, such as R-squared and adjusted R-squared, and test the significance of the coefficients using t-tests and F-tests2.

Option A is incorrect, as logistic regression is used to fit a regression model that describes the relationship between one or more predictor variables and a binary response variable. Use when: The response variable is binary ñ it can only take on two values1. Ice cream consumption is not a binary variable, but rather a continuous numeric variable.

Option C is incorrect, as Cox regression is used to fit a regression model that describes the relationship between one or more predictor variables and a survival time response variable. Use when: The response variable is the time until an event of interest occurs, such as death, failure, or recovery3. Ice cream consumption is not a survival time variable, but rather a continuous numeric variable.

Option D is incorrect, as polynomial regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Use when:

The relationship between the predictor variable(s) and the response variable is non-linear1. If there is no evidence of non-linearity in the data, polynomial regression may not be appropriate, as it may overfit the data and produce unreliable estimates.

Answer C) To confirm important details before dashboard development begins.

Explanation:

A dashboard mockup is a prototype of a finished dashboard directly in the product. It is a way to visualize the layout, design, and functionality of the dashboard before it is built with real data and code. A dashboard mockup can help the analyst to confirm important details with the user, such as the business objectives, the key performance indicators, the data sources, the filters, the charts, and the interactivity. By creating a dashboard mockup, the analyst can get immediate feedback and validation from the user, and avoid wasting time and resources on developing a dashboard that does not meet the user's expectations or needs1.

After conducting a review of the business questions, the next step for the analyst is to determine the data needs and sources for analysis. This involves identifying the relevant data elements, variables, and metrics that are required to answer the business questions, as well as the data sources, formats, and quality that are available to access and use. This step will help the analyst to plan the data collection, preparation, and integration processes, as well as to assess the feasibility and limitations of the analysis1.

After writing the report, the most appropriate next step for the analyst is to complete a check for quality in the report. This involves reviewing the report for accuracy, clarity, completeness, consistency, and relevance. The analyst should ensure that the report addresses the director's business questions and objectives, that the data and analysis are correct and reliable, that the tables and graphs are well-designed and easy to understand, that the descriptions and summary are concise and informative, and that there are no errors or inconsistencies in the report. A quality check will help the analyst to improve the presentation and communication of the report, as well as to avoid any misunderstandings or misinterpretations by the director1.

Discrete data are data that can only assume specific values that are countable and distinct. For example, the number of books, the number of heads in a coin toss, or the number of patients in a hospital are discrete data. Discrete data cannot have fractional or decimal values, and there are clear spaces between the possible values12.

Continuous data are data that can assume any value within a range and can be meaningfully divided into smaller parts. For example, the weight, height, length, time, or temperature are continuous data. Continuous data can have fractional or decimal values, and there are infinite numbers of possible values between any two points12.