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Test EMC D-DS-FN-23 Valid | Regualer D-DS-FN-23 Update

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

Topic
Details

Topic 1

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 2

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 3

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 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.

Topic 5

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.

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EMC Dell Data Science Foundations Sample Questions (Q324-Q329):

NEW QUESTION # 324
Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?

A. The data is unformatted.
B. There are categorical variables in the model.
C. There are missing values in the data.
D. There is not enough data to create a test set.

Answer: D

NEW QUESTION # 325
In K-means clustering, what is a graph of the WSS versus the value of K used to help determine?

A. Average distance between observations
B. 'Optimal number of clusters
C. Optimal distance between clusters
D. Average distance between clusters

Answer: B

Explanation:
A graph of the WSS (Within-Cluster Sum of Squares) versus the value of K helps determine the optimal number of clusters. The "elbow" point on the graph represents the value of K at which adding more clusters no longer significantly improves the WSS.

NEW QUESTION # 326
You are using MADlib for Linear Regression analysis.
Which value does the statement return?
SELECT (linregr(depvar, indepvar)).r2 FROM zeta1;

A. Goodness of fit
B. Coefficients
C. P-value
D. Standard error

Answer: A

NEW QUESTION # 327
Refer to the exhibit.

You have scored your Naive bayesian classifier model on a hold out test data for cross validation and determined the way the samples scored and tabluated them as shown in the exhibit.
What are the Precision and Recall rate of the model?

A. Precision =262/288 Recall = 262/277
B. Precision = 262/277 Recall = 262/288
C. Precision = 288/262 Recall = 277/262
D. Precision = 277/262 Recall = 288/262

Answer: B

NEW QUESTION # 328
What provides the means for matching and manipulating text strings in SQL?

A. TF-IDF
B. Association rules
C. Regular expressions
D. PACF

Answer: C

NEW QUESTION # 329
......

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