[Q156-Q170] Master 2026 Latest The Questions Dell Data Science and Pass D-DS-FN-23 Real Exam!

Master 2026 Latest The Questions Dell Data Science and Pass D-DS-FN-23 Real Exam!

Penetration testers simulate D-DS-FN-23 exam PDF

Q156. What type of variable is the dependent variable from a logistic regression?

 
 
 
 

Q157. What is the primary function of the NameNode in Hadoop?

 
 
 
 

Q158. Refer to the Exhibit.

You are working on creating an OLAP query that outputs several rows of with summary rows of subtotals and grand totals in addition to regular rows that may contain NULL as shown in the exhibit.
Which function can you use in your query to distinguish the row from a regular row to a subtotal row?

 
 
 
 

Q159. You are using MADlib for Linear Regression analysis.
Which value does the statement return?
SELECT (linregr(depvar, indepvar)).r2 FROM zeta1;

 
 
 
 

Q160. In logistic regression modeling, what is the commonly assigned probability threshold used to assign a class label?

 
 
 
 

Q161. Refer to the exhibit.

In association rules, for itemsets X and Y, which expression defines leverage?

 
 
 
 

Q162. In which lifecycle stage are test and training data sets created?

 
 
 
 

Q163. What is a benefit of Spark in-memory data processing as opposed to using MapReduce?

 
 
 
 

Q164. What is a property of window functions in SQL commands?

 
 
 
 

Q165. A data scientist is given an R data frame, “empdata”, with the columns Age, Salary, Occupation, Education, and Gender. The data scientist would like to examine only the Salary and Occupation columns for ages greater than 40.
Which command extracts the appropriate rows and columns from the data frame?

 
 
 
 

Q166. Before you build an ARMA model, how can you tell if your time series is weakly stationary?

 
 
 
 

Q167. Refer to the exhibit.

Click on the calculator icon in the upper left corner. You are going into a meeting where you know your manager will have a question on your dataset — specifically relating to customers that are classified as renters with good credit status.
In order to prepare for the meeting, you create a rule: RENTER => GOOD CREDIT.
What is the confidence of the rule?

 
 
 
 

Q168. In a t-test with unknown variance, what values are used to calculate the t-statistic?

 
 
 
 

Q169. You build a decision tree to classify five different types of customers based on their browsing history from a sample of 500. The resulting decision tree has 17 layers. One of the leaf nodes has only three customers.
What do you conclude?

 
 
 
 

Q170. Based on the exhibit, the table shows the values for the input Boolean attributes A, B, and C.
In addition, the exhibit shows the values for the output attribute “class”.

Which decision tree is valid for the data?

 
 
 
 

EMC D-DS-FN-23 Exam Syllabus Topics:

Topic Details
Topic 1
  • Advanced Analytics for Big Data – Technology and Tools: This section of the exam measures the skills of a Data Science Enthusiast and addresses the technological challenges associated with Big Data. It introduces tools and technologies such as MapReduce, Hadoop, the Hadoop ecosystem, in-database analytics, SQL essentials, and advanced SQL techniques like window functions and MADlib.
Topic 2
  • Initial Analysis of the Data: This section of the exam measures the skills of a Data Science Enthusiast and focuses on the first steps in analyzing data. It explains how basic R commands are used for exploration, discusses important statistical measures and visualizations, and describes hypothesis testing techniques for evaluating models.
Topic 3
  • Big Data, Analytics, and the Data Scientist Role: This section of the exam measures the skills of a Data Science Enthusiast and covers the basic concepts of Big Data, including its defining characteristics and the business drivers behind its rise. It also introduces the role of the Data Scientist, highlighting the critical skills needed in the data science field.
Topic 4
  • Data Analytics Lifecycle: This section of the exam measures the skills of an Entry-Level Data Analyst and explains the purpose and phases of the data analytics lifecycle. It includes understanding key activities and roles involved in the discovery, data preparation, model planning, and model building phases to successfully manage analytics projects.

 

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