Practice Free D-DS-FN-23 Exam Online Questions
You are testing two new weight-gain formulas for puppies. The test gives the results: Control group: 1% weight gain Formula A. 3% weight gain Formula B. 4% weight gain A one-way ANOVA returns a p-value = 0.027
What can you conclude?
- A . Either Formula A or Formula B is effective at promoting weight gain.
- B . Formula B is more effective at promoting weight gain than Formula A.
- C . Formula A and Formula B are both effective at promoting weight gain.
- D . Formula A and Formula B are about equally effective at promoting weight gain.
What is the mandatory Clause that must be included when using Window functions?
- A . OVER
- B . RANK
- C . PARTITION BY
- D . RANK BY
To ensure a successful analytic project, which key role can provide business domain expertise with a deep understanding of the data and key performance indicators?
- A . Business Intelligence Analyst
- B . Project Manager
- C . Project Sponsor
- D . Business User
You have fit a decision tree classifier using 12 input variables. The resulting tree used 7 of the 12 variables, and is 5 levels deep. Some of the nodes contain only 3 data points. The AUC of the model is 0.85.
What is your evaluation of this model?
- A . The tree is probably overfit. Try fitting shallower trees and using an ensemble method.
- B . The AUC is high, and the small nodes are all very pure. This is an accurate model.
- C . The tree did not split on all the input variables. You need a larger data set to get a more accurate model.
- D . The AUC is high, so the overall model is accurate. It is not well-calibrated, because the small nodes will give poor estimates of probability.
You have been assigned to perform a study of the daily revenue effect of a pricing model of online transactions. When is the analytics lifecycle considered completed?
- A . When written documentation has been produced and the code has been handed off to the DBA/operations.
- B . When a model has been completely developed and the results have shown statistically acceptable results.
- C . When the results of the model have been presented to both the internal analytics team and the business owner of the project.
- D . When a model has been completely developed based on both a sample of the data and the entire set of data available.
What is a motivation for using a data analytics lifecycle?
- A . Explores all possible approaches
- B . Limits the amount of data needed
- C . Guarantees a successful project
- D . Creates a repeatable process
What does the R code z <- f[1:10, ] do?
- A . Assigns the first 10 rows of f to the vector z
- B . Assigns the 1st 10 columns of the 1st row of f to z
- C . Assigns a sequence of values from 1 to 10 to z
- D . Assigns the 1st 10 columns to z
In data visualization, what is used to focus the audience on a key part of a chart?
- A . Emphasis colors
- B . Detailed text
- C . Pastel colors
- D . A data table
You have been assigned to perform a study of the daily revenue effect of a pricing model of online transactions. All data currently available to you has been loaded into your analytics database. This includes revenue data, pricing data, and online transaction data.
You discover that all data comes in different levels of granularity. The transaction data has timestamps consisting of day, hour, minutes, and seconds. Pricing is stored at the daily level and revenue data is only reported monthly.
What is the next step?
- A . Report back to the business owner that the current data model does not support the business question.
- B . Interpolate a daily model for revenue from the monthly revenue data.
- C . Aggregate all data to the monthly level in order to create a monthly revenue model.
- D . Disregard revenue as the key reason in the pricing model and create a daily model based on pricing and transactions only.
What are considerations in a data science and Big Data analytics project?
- A . Ignoring executive stakeholders and business users
- B . Applying the latest technologies to demonstrate technical skills
- C . Analysis flexibility and decision making
- D . Building data silos and bypassing data privacy rules