Unique Top-selling SDS Exams - New 2026 DASCA Pratice Exam
DASCA Data Scientist Dumps SDS Exam for Full Questions - Exam Study Guide
NEW QUESTION # 46
Which of the following is NOT a cluster management tool?
- A. Apache Mesos
- B. Zettaset Orchestrator
- C. Apache Hadoop
- D. Apache Ambari
Answer: C
Explanation:
Cluster management tools help in orchestrating and monitoring large-scale distributed computing environments.
Zettaset Orchestrator (A): Commercial tool for Hadoop cluster management.
Apache Mesos (B): A cluster manager that abstracts CPU, memory, and storage to enable fault-tolerant distributed systems.
Apache Ambari (C): An open-source tool for provisioning, managing, and monitoring Hadoop clusters.
Apache Hadoop (D): Not a cluster management tool. Hadoop is a framework for distributed storage and processing (HDFS + MapReduce), not a management tool.
Thus, the correct answer is Option D (Apache Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Hadoop Tools & Cluster Management.
NEW QUESTION # 47
Which of the following is used to summarize a dataset by showing the median, quantiles, and min/max values for each of the variables?
- A. Bar Charts
- B. Histogram
- C. Pie Charts
- D. Scatter Chart
- E. Box Plots
Answer: E
Explanation:
A Box Plot (also called Whisker Plot) is a visualization tool used to summarize data distribution using five- number summary:
Minimum,
First quartile (Q1),
Median (Q2),
Third quartile (Q3),
Maximum.
It also highlights outliers explicitly.
Option A (Box Plots): Correct.
Option B (Pie Charts): Show proportions, not distribution.
Option C (Histogram): Shows frequency distribution but not quartiles/median.
Option D (Scatter Chart): Used for relationships between two variables, not summary statistics.
Option E (Bar Charts): Compare categories, not statistical spread.
Thus, the correct answer is Option A (Box Plots).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization Tools: Box Plots and Statistical Summaries.
NEW QUESTION # 48
Tar is an example of:
- A. None of the above
- B. ARV file format
- C. Text file format
- D. Archive file format
- E. CSV file format
Answer: D
Explanation:
TAR (Tape Archive) is a widely used archive file format in Unix/Linux environments. It is used to combine multiple files into a single archive file (with extension .tar).
Option A: Correct. TAR is specifically designed for archiving.
Option B (CSV): Incorrect. CSV (Comma-Separated Values) is a tabular text data format.
Option C (ARV): Incorrect - no such format.
Option D (Text): Incorrect. Though TAR may contain text files, the TAR format itself is not plain text but an archive format.
Option E: Incorrect since Option A is valid.
Thus, TAR is an Archive file format.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Storage Formats in Data Science & Engineering.
NEW QUESTION # 49
Business Intelligence (BI) is:
- A. BI focuses on descriptive analytics
- B. BI focuses on "What happened?"
- C. Both A and B
- D. Both B and C
- E. BI focuses on reporting on the future state of the business
Answer: C
Explanation:
Business Intelligence (BI) is primarily focused on descriptive analytics and reporting - understanding historical and current business performance.
Option A (Descriptive analytics): Correct. BI uses dashboards, reports, and OLAP tools to summarize what has occurred in the past.
Option B ("What happened?"): Correct. BI answers retrospective questions by analyzing transactional and operational data.
Option C (Future state): Incorrect. Predicting future business outcomes falls under predictive analytics or advanced analytics, not BI.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization & BI: Descriptive Analytics and Reporting.
NEW QUESTION # 50
Which of the following is TRUE for Business Metamorphosis?
- A. Both A and C
- B. The Business Metamorphosis phase helps drive an organization's core business model through the analytic insights gathered as the organization traverses the Big Data Business Model Maturity Index
- C. Business Metamorphosis exercise can uncover Big Data requirements around decisions, analytics and data sources that can be leveraged to transform or metamorphose your organization's business model
- D. The Business Metamorphosis phase is where organizations integrate the insights that they captured about their customers' usage patterns, product performance behaviors, and overall market trends to transform their business models
- E. All of the above
Answer: E
Explanation:
Business Metamorphosis is the most advanced phase in the Big Data Business Model Maturity Index (BDBMMI), where organizations fundamentally transform their business models through analytics-driven insights.
Option A: Correct. This phase helps organizations identify big data requirements related to decisions, analytics, and sources that drive business transformation.
Option B: Correct. Organizations integrate customer usage patterns, product behaviors, and market trends into their decision-making to redesign or innovate their business model.
Option C: Correct. Business Metamorphosis ensures that the core business model evolves continuously, guided by insights derived across maturity stages.
Since all are correct, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Business Model Maturity Index.
NEW QUESTION # 51
The Big Data Vision Workshop process is ideal for organizations who:
- A. Have a wealth of data that they do not know how to monetize
- B. Have a desire to leverage Big Data to transform their business but do not know where and how to start
- C. Both A and B
- D. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
- E. All of the above
Answer: E
Explanation:
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.
NEW QUESTION # 52
Machine learning can be categorized as:
- A. Reinforcement learning
- B. Unsupervised learning
- C. Supervised learning
- D. All of the above
Answer: D
Explanation:
Machine learning (ML) can be broadly divided into three main paradigms:
Supervised Learning (Option A):
Data includes labeled outputs (e.g., classification, regression).
Goal: Learn a mapping from input to output.
Unsupervised Learning (Option B):
Data has no labels.
Goal: Discover hidden patterns (e.g., clustering, dimensionality reduction).
Reinforcement Learning (Option C):
Agent interacts with an environment and learns by maximizing cumulative rewards through trial and error.
Used in robotics, game AI, and autonomous systems.
Since all three categories are valid, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Paradigms: Supervised, Unsupervised, Reinforcement.
NEW QUESTION # 53
Which of the following is NOT an example of graphical model?
- A. Road maps
- B. Geographical networks
- C. Flow charts
- D. Computer networks
- E. Electrical circuits
Answer: C
Explanation:
Graphical models represent relationships between objects using nodes (entities) and edges (relationships).
Examples include:
Road maps (Option A): Nodes = intersections, Edges = roads.
Electrical circuits (Option B): Nodes = components, Edges = connections.
Computer networks (Option C): Nodes = devices, Edges = connections.
Geographical networks (Option D): Nodes = locations, Edges = transport or connectivity.
However:
Flow charts (Option E): These represent process flows, not structural networks of entities and relationships.
They are procedural diagrams, not graphical models in the statistical/graph-theory sense.
Thus, the correct answer is Option E (Flow charts).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graphical Models and Graph Analysis.
NEW QUESTION # 54
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 # 55
Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized
- A. i, ii
- B. i, iii
- C. None of the above
- D. ii, iii
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 # 56
What is Scrumban?
- A. It combines the principles of Scrum and Kanban into a pull-based system
- B. It combines the principles of Scrum and Kanban into a push-based system
- C. It is Kanban
- D. It is Scrum
Answer: A
Explanation:
Scrumban is a hybrid Agile methodology that merges Scrum and Kanban to take advantage of the strengths of both.
From Scrum, Scrumban adopts structured sprint planning, roles, and iterative review cycles.
From Kanban, it borrows the visual board system, continuous workflow management, and the pull-based approach, where tasks are pulled into the workflow only when capacity is available.
The pull-based system ensures that teams do not overload themselves and helps manage work-in-progress (WIP) effectively. This makes Scrumban particularly suitable for projects with frequent changes, ongoing maintenance tasks, or teams transitioning from Scrum to Kanban.
Thus, the correct answer is Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Project Management Techniques for Data Science.
NEW QUESTION # 57
Which of the following is NOT a correct situation to use Agile?
- A. When the final product isn't clearly defined
- B. When changes need to be implemented during the entire process
- C. None of the above
- D. When clients/stakeholders need to be able to change the scope
Answer: C
Explanation:
Agile methodology is widely adopted in data science projects because these projects often involve uncertain goals, exploratory analysis, and changing requirements. Agile thrives in environments where iteration, collaboration, and adaptability are necessary.
Option A: True for Agile. If the final product is unclear (common in data science), Agile works well because it allows incremental discovery and iterative prototyping.
Option B: True for Agile. Agile frameworks (Scrum, Kanban) emphasize flexibility, which means the scope can evolve as stakeholders learn more from data and models.
Option C: True for Agile. Agile welcomes continuous changes through iterative sprints and feedback loops.
This adaptability is crucial in machine learning model development where data insights often reshape project direction.
Since all three situations are valid for Agile, the correct answer to "Which is NOT correct?" is None of the above (Option D).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science & Agile Methodologies in Data Projects.
NEW QUESTION # 58
Which of the following is main Machine Learning Library in Python?
- A. None of the above
- B. SciPy
- C. NumPy
- D. Matplotlib
- E. Scikit-learn
Answer: E
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 # 59
Which of the following is a trend analysis component of time series decomposition?
- A. Irregular
- B. Cyclical
- C. Both A and B
- D. Seasonal
- E. All of the above
Answer: E
Explanation:
Time series decomposition breaks down data into components to better understand underlying patterns and support forecasting. The main components are:
Trend: Long-term progression (upward or downward).
Seasonal: Repeating short-term patterns (e.g., monthly or quarterly).
Cyclical (Option A): Medium- to long-term cycles (e.g., business cycles).
Irregular/Residual (Option C): Random, unpredictable variations.
Since trend analysis involves examining cyclical, seasonal, and irregular components, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Time Series Decomposition and Trend Analysis.
NEW QUESTION # 60
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
- A. Normal Curve Equivalent (NCE)
- B. None of the above
- C. Medium Curve Equivalent (MCE)
- D. High Curve Equivalent (HCE)
- E. Trend analysis
Answer: A
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 # 61
Semi-structured data does NOT include:
- A. File systems
- B. Schema-full data
- C. Database system
- D. Scientific data
Answer: B
NEW QUESTION # 62
Which of the following is True about Time Series Analysis?
- A. Projecting the value of the time series at future points in time, such as a stock whose price we want to predict
- B. Predicting when/whether an event will occur, such as a failure of the machine generating the data
- C. Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through
- D. Both A and B
- E. All of the above
Answer: E
Explanation:
Time Series Analysis (TSA) is the process of analyzing data collected sequentially over time to extract meaningful insights.Applications include:
Option A: Correct. Event prediction (e.g., failure detection in IoT or predictive maintenance).
Option B: Correct. Forecasting future values (e.g., stock price, sales forecasting).
Option C: Correct. Pattern discovery in large-scale time series datasets using clustering, anomaly detection, or seasonality detection.
Since all three are true, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Time Series Analysis and Forecasting.
NEW QUESTION # 63
Which of the following is NOT a part of Internal Process Optimization?
- A. Business Monitoring
- B. None of the above
- C. Business Metamorphosis
- D. Business Insights
- E. Business Optimization
Answer: C
Explanation:
Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.
Typical components of IPO include:
Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.
Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.
Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.
However:
Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.
Therefore, the correct answer is Option B (Business Metamorphosis).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Internal Process Optimization.
NEW QUESTION # 64
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