Which of the following would be considered non-personally identifiable information?
Correct Answer: A
Non-personally identifiable information (non-PII) is any data that cannot be used to identify, contact, or locate a specific individual, either alone or combined with other sources. Non-PII can include aggregated statistics, anonymous data, device identifiers, IP addresses, cookies, and other types of information that do not reveal the identity or location of a person. Cell phone device name is an example of non-PII, as it does not reveal any personal information about the owner or user of the device. Therefore, the correct answer is A. Reference: What is Non-Personally Identifiable Information (Non-PII)? | Definition and Examples, What is Personally Identifiable Information (PII)? | Definition and Examples
Question 137
A marketing analytics team received customer transaction data from two different sources. The data is complete and accurate; however, the field names appear to be inconsistent. Given the following tables: Which of the following is considered best practice if the team wants to consolidate the files and conduct further analysis?
Correct Answer: C
Question 138
A data analyst has been asked to create an ad-hoc sales report for the Chief Executive Officer (CEO). Which of the following should be included in the report?
Correct Answer: C
The report for the CEO should include YTD total sales, as this will provide a high-level overview of the sales performance of the company and show how it is meeting its annual goals. The other options are not appropriate for the CEO, as they are either too detailed or irrelevant for the report. The sales representatives' home addresses, line-item SKU numbers, and customers' first and last names are not related to the sales performance and might compromise the privacy and security of the data. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy
Question 139
A data analyst is working with a team to create a dashboard for a client who requires on-demand access. Which of the following is the best delivery method to support the clients' requirement?
Correct Answer: C
Explanation The best delivery method to support the client's requirement is C. Subscription. Short explanation: A subscription is a delivery method that allows the client to access the dashboard on-demand, whenever they need it. A subscription can be set up by the data analyst or the client themselves, and it can be configured to send an email notification when the dashboard is updated or refreshed. A subscription also allows the client to view the dashboard online or download it as a file format of their choice12 A: Email is not the best delivery method because it does not allow the client to access the dashboard on-demand. Email deliveries are sent at a fixed time or frequency, and they may not reflect the latest data or changes in the dashboard. Email deliveries also have limitations on the file size and format of the dashboard attachments1 B: Scheduled is not the best delivery method because it does not allow the client to access the dashboard on-demand. Scheduled deliveries are similar to email deliveries, except that they are triggered by a specific event or condition, such as a data update or a threshold value. Scheduled deliveries also have the same limitations as email deliveries on the file size and format of the dashboard attachments1 D: Static is not the best delivery method because it does not allow the client to access the dashboard on-demand. Static deliveries are one-time deliveries that are manually generated by the data analyst or the client. Static deliveries do not update or refresh automatically, and they may become outdated or irrelevant over time. Static deliveries also have limitations on the file size and format of the dashboard files3
Question 140
A data analyst is attempting to understand how ice cream consumption is affected by different attributes. such as cost, temperature. and income level. Which of the following regression analyses should the data analyst perform to understand this relationship?
Correct Answer: B
Explanation answer: B. Ordinary least squares 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 data. 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 - 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.
