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DASCA Senior Data Scientist Sample Questions (Q20-Q25):
NEW QUESTION # 20
Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?
- A. Trend Analysis
- B. None of the above
- C. Box Plots
- D. Scatter Plot
- E. Histogram
Answer: C
Explanation:
Box plots (or Whisker plots) are statistical graphics that represent data distribution through:
Minimum, First Quartile (Q1), Median, Third Quartile (Q3), and Maximum.
Outliers are plotted as individual points beyond the whiskers.
This makes them particularly powerful for:
Identifying outliers in data.
Comparing distributions across categories.
Understanding variability in data.
Option A (Trend Analysis): Shows temporal patterns, not individual outliers.
Option C (Histogram): Shows frequency distribution but does not explicitly highlight outliers.
Option D (Scatter Plot): Shows relationships between variables but doesn't focus on statistical outliers in one distribution.
Thus, the correct answer is Option B (Box Plots).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization Tools: Box Plots for Outlier Detection.
NEW QUESTION # 21
The Big Data Vision Workshop process is ideal for organizations who:
- A. Both A and B
- B. Have a wealth of data that they do not know how to monetize
- C. All of the above
- D. Have a desire to leverage Big Data to transform their business but do not know where and how to start
- E. 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
Answer: C
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 # 22
Spark should be used when:
- A. None of the above
- B. Both A and B
- C. Data is not massive
- D. Data is massive
Answer: D
Explanation:
Apache Spark is a distributed data processing engine optimized for big data scenarios. It is specifically designed to handle:
Large-scale datasets spread across clusters.
Massive streaming or batch data pipelines.
Machine learning and graph processing at scale.
Option A: Correct - Spark excels when data is massive and distributed.
Option B: Incorrect - Spark is overkill for small data (Pandas, NumPy, or scikit-learn would be more efficient).
Option C: Incorrect - Spark is not optimized for small datasets.
Option D: Incorrect - since A is valid.
Thus, Spark should be used when data is massive # Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Processing: Apache Spark Applications.
NEW QUESTION # 23
What is Scrumban?
- A. It is Kanban
- B. It combines the principles of Scrum and Kanban into a pull-based system
- C. It combines the principles of Scrum and Kanban into a push-based system
- D. It is Scrum
Answer: B
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 # 24
What is Scrum?
- A. Agile is a subset of Scrum
- B. None of the above
- C. Scrum is a subset of Agile
- D. Scrum and Agile are the same
Answer: C
Explanation:
Scrum is a framework used to implement Agile principles. Agile itself is the overarching philosophy or mindset, while Scrum is one of the most popular frameworks that apply Agile values in practice.
Option A (Correct): Scrum is indeed a subset of Agile. Agile defines the principles (from the Agile Manifesto), and Scrum provides the structure (roles, artifacts, ceremonies).
Option B: Incorrect. Agile is broader and not a subset of Scrum.
Option C: Incorrect. Scrum and Agile are not the same; Agile is the philosophy, Scrum is a methodology under Agile.
Option D: Incorrect because Option A is valid.
Thus, the correct answer is Option A: Scrum is a subset of Agile.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile and Scrum in Data Science Projects.
NEW QUESTION # 25
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