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DASCA Senior Data Scientist Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which of the following is correct?
i. LaTeX is used to publish work in a scientific journal
ii. LaTeX is a markup language that can be compiled into formatted documents iii. LaTeX is for publishing scientific papers

A. i, iii
B. i, ii
C. i, ii, iii
D. ii, iii

Answer: C

Explanation:
LaTeX is a high-quality typesetting system widely used in academia, particularly in scientific publishing.
Statement i: Correct. LaTeX is widely used to prepare manuscripts for scientific journals, theses, and technical reports.
Statement ii: Correct. LaTeX is a markup language (similar to HTML in concept) that compiles into formatted PDFs/documents.
Statement iii: Correct. LaTeX is a standard for publishing scientific papers due to its ability to handle complex mathematical equations, references, and formatting.
Thus, all three statements are true # Option B (i, ii, iii).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools for Data Science: LaTeX for Scientific Documentation.

NEW QUESTION # 14
A burn down chart shows:

A. The number of hours worked after dark
B. The volume of work and features completed
C. The declining energy of the team
D. The rate of reduction of budget for a project

Answer: B

Explanation:
A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.
Option A: Incorrect. Burn down charts don't measure team "energy" or motivation levels.
Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.
Option C: Incorrect. Hours worked after dark is irrelevant.
Option D: Incorrect. Budget reduction is not tracked in burn down charts.
Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Project Management & Agile Tools in Data Science.

NEW QUESTION # 15
Which of the following is TRUE for Tensor?

A. All of the above
B. Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
C. In Tensor, there can be arbitrarily many dimensions to the array
D. Both B and C
E. Tensor is an array of floating-point numbers

Answer: A

Explanation:
A Tensor is a fundamental data structure in modern machine learning frameworks (e.g., TensorFlow, PyTorch). It is best described as a generalization of vectors and matrices to potentially higher dimensions.
Option A: Correct. Tensors typically store numeric values (commonly floating-point numbers) in structured formats.
Option B: Correct. A tensor can have any number of dimensions (rank). For example:
A scalar is a 0-D tensor.
A vector is a 1-D tensor.
A matrix is a 2-D tensor.
Higher-rank tensors can represent images, videos, or multidimensional datasets.
Option C: Correct. Tensors are explicitly designed to allow linear algebra operations, which are the foundation of deep learning computations (matrix multiplications, dot products, etc.).
Therefore, since all three statements are true, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning, Deep Learning Concepts; Official DASCA Study Guide.

NEW QUESTION # 16
Which of the following is correct about microservices?

A. All of the above
B. Each service is a new project
C. Each service is independent
D. Each service can be developed in any language that best fits the requirement

Answer: A

Explanation:
Microservices architecture is a software design approach where applications are developed as a collection of small, loosely coupled, and independently deployable services.
Option A: Correct. Each microservice runs independently and has its own lifecycle, allowing scalability and modularity.
Option B: Correct. Each service can be treated as an independent project with its own repository, build pipeline, and deployment.
Option C: Correct. Microservices can be developed in different programming languages or frameworks, chosen based on the best fit for each specific requirement.
Therefore, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Management & Engineering: Microservices in Data Ecosystems.

NEW QUESTION # 17
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?

A. Medium Curve Equivalent (MCE)
B. None of the above
C. Normal Curve Equivalent (NCE)
D. High Curve Equivalent (HCE)
E. Trend analysis

Answer: C

Explanation:
Normal Curve Equivalent (NCE) scores are standardized scores designed to:
Range between 1 and 99.
Be comparable to percentile ranks but with the advantage of equal-interval properties like Z-scores.
This makes NCE scores useful in educational assessments, survey analysis, and statistical modeling.
Option A (Trend analysis): Incorrect. Not related to score standardization.
Option B (Correct): NCE fits the definition perfectly.
Option C (HCE) & D (MCE): Not recognized standard measures in statistics.
Option E: Incorrect, since Option B is valid.
Thus, the correct answer is Option B: Normal Curve Equivalent (NCE).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Methods in Data Science: Z-scores, Percentiles, and NCE.

NEW QUESTION # 18
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