Free Dell Data Science Foundations Practice Questions

Sample Practice Questions

Try these 10 sample questions from the Dell Data Science Foundations practice bank. Each includes the answer and explanation. Continue with the interactive practice above to review the full question set.

1. 1A data scientist must integrate customer records, image files, and sensor readings from separate repositories. Which big-data characteristic is most directly illustrated by these inputs?

   1. A.Volume
   2. B.Velocity
   3. C.VarietyCorrect answer: C
   4. D.Variability

   Explanation: NIST states that "The variety characteristic represents the need to analyze data from multiple repositories, domains, or types." The different repositories and data types illustrate variety; volume concerns size, velocity concerns flow rate, and variability concerns changes in the dataset.

   Reference: NIST Big Data Interoperability Framework: Volume 1, Definitions — Section 3.2.3, page 11

2. 2A team is following the data analytics lifecycle presented in the cited course textbook and has completed the second phase, data preparation. Which phase comes next in the published sequence?

   1. A.Discovery
   2. B.Model planningCorrect answer: B
   3. C.Model building
   4. D.Communicate results

   Explanation: The publisher's contents list labels data preparation as "2.3 Phase 2: Data Preparation 36" and model planning as "2.4 Phase 3: Model Planning 42". Model planning follows data preparation in this sequence.

   Reference: Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data — Table of contents, Chapter 2

3. 3An R vector x contains c(1, 2, 3, 100, NA). Which command computes the untrimmed arithmetic mean of its observed values?

   1. A.mean(x, trim = 0.25, na.rm = TRUE)
   2. B.mean(x, trim = 0, na.rm = FALSE)
   3. C.mean(x, trim = 0, na.rm = TRUE)Correct answer: C
   4. D.mean(x, trim = 0.25, na.rm = FALSE)

   Explanation: The R documentation says na.rm indicates whether "NA values should be stripped before the computation proceeds." Setting it to TRUE omits the missing value; trim = 0 retains all observed values.

   Reference: R Documentation: Arithmetic Mean — Description and Arguments, na.rm

4. 4A data scientist fits k-means to numeric customer observations. What quantity is the method intended to minimize for a fixed number of clusters?

   1. A.The sum of squared distances to assigned cluster centersCorrect answer: A
   2. B.The sum of squared distances between cluster centers
   3. C.The sum of distances between all pairs of observations
   4. D.The sum of squared distances to the global data mean

   Explanation: R describes k-means as partitioning points so that "the sum of squares from points to the assigned cluster centres is minimized." The objective is within-cluster squared distance.

   Reference: R Documentation: K-Means Clustering — Details

5. 5A data scientist designs a distributed MapReduce job that transforms input records into intermediate key-value pairs. Which component carries out that transformation?

   1. A.MapperCorrect answer: A
   2. B.Reducer
   3. C.Partitioner
   4. D.RecordReader

   Explanation: The tutorial states that "Mapper maps input key/value pairs to a set of intermediate key/value pairs." That is the requested transformation.

   Reference: Apache Hadoop MapReduce Tutorial — Payload: Mapper

6. 6One evaluation identifies several distinct program areas ready for improvement. How does the CDC guidance recommend organizing the follow-up action plans?

   1. A.Create a plan for each improvement objective.Correct answer: A
   2. B.Create a plan for each stakeholder category.
   3. C.Create a plan for each data collection method.
   4. D.Create a plan for each outcome measurement.

   Explanation: The guidance says "we recommend creating an action plan for each improvement objective" when one evaluation identifies multiple areas to improve.

   Reference: Learning and Growing through Evaluation: Implementing Evaluations — Appendix K, page K-1

7. 7An analytics team needs a generalist to connect its business objective, domain processes, statistical analysis, and computing approach across the entire data lifecycle. In the NIST terminology, which role best matches this responsibility?

   1. A.Data engineer
   2. B.Semantic specialist
   3. C.Horizontal data scientistCorrect answer: C
   4. D.Machine learning specialist

   Explanation: NIST defines a horizontal data scientist as "a generalist who understands enough of the multiple disciplines of mission need, the domain processes that produced the data, math and statistics, and computer science (or software and systems engineering)." That breadth supports the team's end-to-end work; the other options describe narrower specialist roles.

   Reference: NIST Big Data Interoperability Framework: Volume 1, Definitions — Section 5.2, page 28

8. 8An organization is choosing how to structure an analytics project from data collection through use of its results. Which purpose does NIST assign to the data analytics lifecycle?

   1. A.Turn raw data into knowledge that can guide action.Correct answer: A
   2. B.Turn raw data into recurring operational reports.
   3. C.Turn raw data into centrally retained audit records.
   4. D.Turn raw data into standardized exchange formats.

   Explanation: NIST describes the lifecycle as driven by the "need to transform raw data into actionable knowledge". Reporting, retention, and exchange can be useful data activities, but the cited lifecycle covers the broader analytics purpose.

   Reference: NIST Big Data Interoperability Framework: Volume 1, Definitions — Section 5.3, page 29

9. 9An analyst sorts five daily transaction counts: 2, 3, 5, 8, and 12. What is the median count?

   1. A.3
   2. B.5Correct answer: B
   3. C.6
   4. D.8

   Explanation: NIST defines the median of an odd-sized sorted sample as "Y_{(N+1)/2}". With five observations the middle position is the third, so the median is 5.

   Reference: NIST/SEMATECH e-Handbook of Statistical Methods: Measures of Location — Median definition

10. 10A one-dimensional k-means cluster contains the observations 2, 4, and 9. At a converged minimum, what is its cluster center?

    1. A.4
    2. B.5Correct answer: B
    3. C.5.5
    4. D.15

    Explanation: The R documentation says that at the minimum "all cluster centres are at the mean of their Voronoi sets". The mean of the three assigned values is (2 + 4 + 9) / 3 = 5.

    Reference: R Documentation: K-Means Clustering — Details