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The second format is a web-based practice exam which offers a flexible and accessible option for students trying to assess and improve their preparation for the DASCA Certification Exams. The SDS web-based practice test can be accessed online through browsers like Firefox, Microsoft Edge, Google Chrome, and Safari. Customers need a stable internet connection in order to access web-based formats easily without facing issues.

DASCA Senior Data Scientist Sample Questions (Q25-Q30):

NEW QUESTION # 25
Which of the following is main Machine Learning Library in Python?

Answer: B

Explanation:
Python supports multiple libraries for scientific computing and data analysis, but the primary machine learning library is:
Scikit-learn (Option B): Provides a wide range of machine learning algorithms for classification, regression, clustering, model evaluation, and preprocessing. It is the core ML library in Python.
NumPy (Option A): Provides numerical computing and array operations, essential for ML but not a machine learning library itself.
Matplotlib (Option C): Used for data visualization.
SciPy (Option D): Supports scientific computing and numerical methods, not focused on ML models.
Therefore, the correct answer is Option B (Scikit-learn).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Python Libraries for Machine Learning.


NEW QUESTION # 26
Which of the following is the common evolutionary algorithm of neural networks?

Answer: B

Explanation:
Evolutionary algorithms are optimization techniques inspired by natural selection. They are often applied in neural network optimization for evolving architectures, weights, or hyperparameters.
Genetic Algorithm (Option A): Uses selection, crossover, and mutation to evolve neural network parameters.
Genetic Programming (Option B): Evolves computer programs or expressions; can be applied to evolve neural network structures.
Evolution Strategy (Option C): Focuses on optimization using mutation and adaptation of parameters.
Since all are used as evolutionary approaches in neural networks, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Neural Networks: Evolutionary Algorithms in Machine Learning.


NEW QUESTION # 27
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

Answer: B

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 # 28
OCR (Optical Character Recognition) is an application used for:

Answer: C

Explanation:
Optical Character Recognition (OCR) is the process of automatically recognizing and converting different types of documents - such as scanned paper documents, PDFs, or images - into editable and searchable text.
OCR systems use Machine Learning (ML) and Computer Vision techniques to detect and classify patterns of characters in images.
Algorithms like Convolutional Neural Networks (CNNs) are commonly used for image-based OCR.
While OCR may indirectly contribute to data mining or big data workflows, the core application is based on machine learning, where models are trained to classify and recognize text patterns.
Thus, OCR is primarily a Machine Learning application, making Option B correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: OCR and Pattern Recognition.


NEW QUESTION # 29
Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized

Answer: A

Explanation:
Images are broadly categorized based on how they store visual information:
Rasterized images (Option i):
Composed of a grid of pixels (bitmap).
Each pixel has color information.
Examples: JPEG, PNG, BMP.
Best for photos or complex visuals.
Vectorized images (Option ii):
Composed of paths defined by mathematical formulas.
Scalable without quality loss.
Examples: SVG, EPS, AI.
Best for logos, icons, and illustrations.
Sectorized images (Option iii):
Not a standard category in computer graphics.
Thus, image files are categorized into Rasterized and Vectorized, making Option A (i, ii) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Types & Multimedia Data Management.


NEW QUESTION # 30
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