D-DS-FN-23: Data Science FoundationsDemo

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%

How to Use This Practice Exam

  1. Browse — Read each question, select your answer, and reveal the explanation.
  2. Exam Mode — Simulate real exam conditions with a timed session and score report.
  3. Learn Mode — Spaced repetition schedules questions you struggle with for long-term retention.

Download the Full Exam PDF

Get every question and answer in a clean, printable PDF built for offline study. Purchase once, keep permanent access, and re-download the latest version anytime.

Get PDF
Learn More

Last updated December 2, 2025 at 12:15 PM

Topic filter
Retired questions
Question sort

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?

  • A Variable B; information gain is 0.1
  • B Variable B; information gain is 0.4
  • C Variable A; information gain is 0.1
  • D Variable A; information gain is 0.5
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.

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ2

Initial Analysis of the Data

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

  • A Both structures can contain only integers
  • B Both structures can only contain one data type
  • C Both structures can store multiple data types
  • D Both structures must be 2-dimensional
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.

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ3

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

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

  • A Ordinal
  • B Numeric
  • C Text
  • D Nominal
Explanation

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

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ4

Initial Analysis of the Data

What is a recommended use case for using regular expressions?

  • A Linear regression
  • B Decision trees
  • C Logistic regression
  • D In-database text analysis
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.

Community Discussion

No comments yet. Be the first to start the discussion!

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?

  • A Use a scatter plot to obtain a better picture
  • B Use a histogram to obtain a better picture
  • C Apply a square transformation
  • D Apply a logarithmic transformation
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.

Community Discussion

No comments yet. Be the first to start the discussion!

That's the end of the preview

It's free

100% of the questions are free for all users.
No strings attached.

Sign up freeLog in
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
Know a question that should be here? Contribute to this exam
Back home