[Q108-Q123] Get Prepared for Your D-DS-FN-23 Exam With Actual EMC Study Guide!

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Get Prepared for Your D-DS-FN-23 Exam With Actual EMC Study Guide!

Pass Your Next D-DS-FN-23 Certification Exam Easily & Hassle Free

NO.108 Refer to the exhibit.

You are building a decision tree. In this exhibit, four variables are listed with their respective values of info-gain.
Based on this information, on which attribute would you expect the next split to be in the decision tree?

 
 
 
 

NO.109 When building a K-means clustering model, you notice that the clusters did not segment on variables that you expected. What should you do?

 
 
 
 

NO.110 You have the following corpus of texts:
“The cat hit the dog.”
“The dog bit the mail carrier.”
“The mail carrier chased the truck.”
“The truck hit the wall while avoiding the dog that chased the cat.”
“The cat climbed the wall.”
If the tf-idf metric is used to score relevance for search and retrieval, which term has the highest discriminatory power?

 
 
 
 

NO.111 A data scientist is preparing a presentation for a meeting with the project’s business sponsors. The distribution of per-sale revenue is an important finding from the analysis. The graphics illustrate four ways to plot the per-sale revenue distribution..”

Which graphic is most appropriate for the sponsor presentation?

 
 
 
 

NO.112 You are using a Logistic Regression model to determine if an applicant’s gender is a factor in determining whether or not they receive a bank loan. When you plot the results, you notice that the regression coefficient is zero.
What can be determined?

 
 
 
 

NO.113 Refer to the exhibit.

In the exhibit, a correlogram is provided based on an autocorrelation analysis of a sample dataset.
What can you conclude based only on this exhibit?

 
 
 
 

NO.114 Refer to the exhibit.

You have plotted the distribution of savings account sizes for your bank.
How would you proceed, based on this distribution?

 
 
 
 

NO.115 Which word or phrase completes the statement; “Discovering relationships is to Association Rules as generating forecasts is to __________.”?

 
 
 
 

NO.116 You are having a discussion with a business colleague. The colleague mentions that they want to perform K-means clustering on text file data stored in HDFS.
Which tool should be recommended?

 
 
 
 

NO.117 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?

 
 
 
 

NO.118 What is Hadoop?

 
 
 
 

NO.119 The graphic shows the values for the input Boolean attributes “A”, “B”, and “C”. In addition, the graphic shows the values for the output attribute “class”.

Which Decision Tree is valid for the data?

 
 
 
 

NO.120 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?

 
 
 
 

NO.121 On analyzing the results of a K-means clustering output, you noticed that splits on variables you expected to see were not observed.
What actions should be taken?

 
 
 
 

NO.122 In the Map Reduce framework, what is the purpose of the Reduce function?

 
 
 
 

NO.123 When creating a project sponsor presentation, what is the main objective?

 
 
 
 

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

Topic Details
Topic 1
  • 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 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
  • Advanced Analytics – Theory, Application, and Interpretation of Results for Eight Methods: This section of the exam measures the skills of an Entry-Level Data Analyst and covers foundational knowledge in various advanced analytics methods. Topics include the theory, application, and interpretation of K-means clustering, association rules, linear and logistic regression, naïve Bayesian classifiers, decision trees, time series analysis, and text analytics.
Topic 4
  • Operationalizing an Analytics Project and Data Visualization Techniques: This section of the exam measures the skills of an Entry-Level Data Analyst and explains best practices for communicating findings and operationalizing analytics projects. It covers effective methods for presenting projects to various audiences and emphasizes the importance of planning and creating impactful data visualizations.

 

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