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Pdf D-DS-FN-23 Exam Dump | Latest D-DS-FN-23 Study Notes

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

Topic
Details

Topic 1

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 2

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.

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

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 5

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.

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EMC Dell Data Science Foundations Sample Questions (Q301-Q306):

NEW QUESTION # 301
In which lifecycle stage are test and training data sets created?

A. Discovery
B. Model building
C. Data preparation
D. Model planning

Answer: B

NEW QUESTION # 302
Refer to the graphic.

How would you run the MADlib kmeans function?

A. UPDATE madlib.kmeans SET centroids = madlib.kmeans('km_sample', 'coords', ... );
B. SELECT * FROM madlib.kmeans('km_sample', 'coords', ... );
C. INSERT INTO madlib.kmeans('km_sample', 'coords', ... );
D. ./madlib kmeans run -parallel -source "km_sample.coords" ...

Answer: B

NEW QUESTION # 303
Refer to the exhibit.

You have created a density plot of purchase amounts from a retail website as shown.
What should you do next?

A. Reduce the sample size of the purchase amount data used to create the plot
B. Recreate the density plot using a log normal distribution of the purchase amount data
C. Recreate the plot using the barplot() function
D. Use the rug() function to add elements to the plot

Answer: B

NEW QUESTION # 304
In logistic regression modeling, what is the commonly assigned probability threshold used to assign a class label?

A. 0.9
B. 0.5
C. 0.25
D. 0.1

Answer: B

NEW QUESTION # 305
What does the R code z <- f[1:10, ] do?

A. Assigns a sequence of values from 1 to 10 to z
B. Assigns the first 10 rows of f to the vector z
C. Assigns the 1st 10 columns of the 1st row of f to z
D. Assigns the 1st 10 columns to z

Answer: B

NEW QUESTION # 306
......

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