D-DS-FN-23: Data Science Foundations

By Dell · Browse Mode
About the Exam

This exam covers the data analytics lifecycle, the role of the data scientist, exploratory analysis in R, statistics for model building and evaluation, advanced analytics methods, big data tools such as Hadoop and MapReduce, and data visualization. It is aimed at candidates preparing for foundational data science and big data analytics work. Passing demonstrates practical foundation skills needed to contribute to analytics projects as a data scientist.

Exam Topics

  • Big Data, Analytics, and the Data Scientist Role5%
  • Data Analytics Lifecycle8%
  • Initial Analysis of the Data15%
  • Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods40%
  • Advanced Analytics for Big Data - Technology and Tools22%
  • Operationalizing an Analytics Project and Data Visualization Techniques10%

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Last updated December 2, 2025 at 12:15 PM

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QuestionQ1

Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods

A decision tree is being constructed. An internal node is being assessed for partitioning on variables A and B. The internal node has an entropy of 0.8, and each variable has the following entropy:

  • Variable A: 0.5
  • Variable B: 0.4

Which variable will partition the data, and what is its information gain?

Explanation

Information gain is the parent-node entropy minus the entropy after the split. Variable B produces an information gain of 0.8 − 0.4 = 0.4, which is greater than the gain from Variable A.

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QuestionQ2

Initial Analysis of the Data

What similarity do the matrix and array data structures share in R?

Explanation

Matrices and arrays in base R are homogeneous data structures: all their elements have one underlying data type. Matrices are limited to two dimensions, while arrays can have two or more dimensions.

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QuestionQ3

Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods

For which type of data should K-means clustering be used?

Explanation

K-means assigns observations to clusters by calculating distances to numeric centroids and updating those centroids with means. It therefore requires numeric data.

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QuestionQ4

Initial Analysis of the Data

What is a recommended use case for using regular expressions?

Explanation

Regular expressions define patterns for finding, extracting, validating, or manipulating textual data. In-database text analysis can use these patterns directly to analyze text values.

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QuestionQ5

Initial Analysis of the Data

After creating a density plot, you notice that the data has a long right-hand tail. What can you do to make the dataset more normally distributed?

Explanation

A logarithmic transformation compresses high values disproportionately, reducing the positive (right) skew produced by a long right-hand tail and often making the distribution closer to normal.

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Topics covered
Big Data, Analytics, and the Data Scientist RoleData Analytics LifecycleInitial Analysis of the DataAdvanced Analytics - Theory, Application, and Interpretation of Results for Eight MethodsAdvanced Analytics for Big Data - Technology and ToolsOperationalizing an Analytics Project and Data Visualization Techniques
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