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Dell EMC D-DS-FN-23 Exam Syllabus

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Before starting your D-DS-FN-23 exam preparation, it is recommended to review the complete Dell EMC Dell Certified Data Science Foundations exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free D-DS-FN-23 questions. We also provide premium D-DS-FN-23 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Dell EMC
Vendor
D-DS-FN-23
Exam Code
59
Total Questions
6
Total Exam Domains

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D-DS-FN-23 EXAM QUESTIONS

Dell EMC D-DS-FN-23 Exam Objectives

Section 1: Big Data, Analytics, and the Data Scientist Role
Weight:
5%
• Define and describe the characteristics of Big Data
• Describe the business drivers for Big Data analytics and data science
• Describe the Data Scientist role and related skills
Section 2: Data Analytics Lifecycle
Weight:
8%
  • Describe the data analytics lifecycle, purpose, and sequence of phases
  • Discovery - Describe details of this phase, including activities and associated roles
  • Data preparation - Describe details of this phase, including activities and associated roles
  • Model planning - Describe details of this phase, including activities and associated roles
  • Model building - Describe details of this phase, including activities and associated roles
Section 3: Initial Analysis of the Data
Weight:
15%
Explain how basic R commands are used to initially explore and analyze the data
• Describe and provide examples of the most important statistical measures and effective
visualizations of data
• Describe the theory, process, and analysis of results for hypothesis testing and its use in
evaluating a model
Section 4: Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods
Weight:
40%
Describe theory, application, and interpretation of results for the following methods:
• K-means clustering
• Association rules
• Linear regression
• Logistic Regression
• Naïve Bayesian classifiers
• Decision trees
• Time Series Analysis
• Text Analytics
Section 5: Advanced Analytics for Big Data - Technology and Tools
Weight:
22%
  •  Describe the technological challenges posed by Big Data
• Describe the nature and use of MapReduce and Apache Hadoop
• Describe the Hadoop ecosystem and related product use cases
• Describe in-database analytics and SQL essentials
• Describe advanced SQL methods: window functions, ordered aggregates, and MADlib
Section 6: Operationalizing an Analytics Project and Data Visualization Techniques
Weight:
10%
  • Describe best practices for communicating findings and operationalizing an analytics project
• Describe best practices for building project presentations for specific audiences
• Describe best practices for planning and creating effective data visualizations
Info
https://learning.dell.com/content/dam/dell-emc/documents/en-english/D-DS-FN-23_Data%20_Scientist_and_Big_Data_Analytics_Foundations_Exam.pdf