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Best SDS Practice Free PDF | High Pass-Rate Mock SDS Exams: Senior Data Scientist

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DASCA Senior Data Scientist Sample Questions (Q86-Q91):

NEW QUESTION # 86
Which of the following is TRUE for "By" analysis?

A. "By" analysis is used to create a collaborative technique to drive alignment between the business users and the data scientists to identify and brainstorm variables and metrics that might be better predictors of business performance.
B. Both B and C
C. All of the above
D. "By" analysis is a technique by which business subject matter experts (SMEs) and the Data Science team could collaborate to uncover new variables and metrics that might be better predictors of business performance.
E. The "By" analysis technique reinforces the process of "thinking like a data scientist."

Answer: C

Explanation:
"By" analysis is one of the foundational approaches recommended in the DASCA Data Scientist Knowledge Framework for structuring problem-solving in data science. The purpose of "By" analysis is to enable data scientists and business stakeholders to think beyond obvious data correlations and uncover deeper drivers of business outcomes.
At its core, the technique reinforces the discipline ofthinking like a data scientist(Option A). This involves reframing business questions into analytical structures and asking "What drives this metricbywhich factors?" For example, customer churn might be analyzedbydemographics, purchase behavior, or service usage. This structured mindset is critical for ensuring scientific rigor in business problem analysis.
In addition, "By" analysis emphasizes collaboration betweenSubject Matter Experts (SMEs)andData Science teams(Option B). SMEs bring contextual domain knowledge, while data scientists bring analytical and statistical expertise. Together, they brainstorm possible explanatory variables or metrics that could become strong predictors of business performance.
Furthermore, the process provides acollaborative bridgebetween business and technical stakeholders (Option C). It ensures that the exploration of data is not isolated in silos but is grounded in both domain insights and advanced analytical methods. This alignment is crucial for building models that are not only technically sound but also relevant and actionable in real-world business contexts.
Since Options A, B, and C are correct and complementary, the best choice isOption E: All of the above.
Reference:DASCA Data Scientist Knowledge Framework (DSKF) -Data Science Process Fundamentals & Collaborative Analysis Techniques(Official DASCA Study & Exam Preparation Guide).

NEW QUESTION # 87
Which of the following is NOT a correct situation to use Agile?

A. When clients/stakeholders need to be able to change the scope
B. When changes need to be implemented during the entire process
C. When the final product isn't clearly defined
D. None of the above

Answer: D

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 # 88
The grid computing environment uses a middleware to:

A. Combine computing resources
B. None of the above
C. Both A and B
D. Divide computing resources

Answer: C

Explanation:
Grid computing is a distributed computing model where resources (CPU, memory, storage) are pooled across multiple systems to solve large-scale problems.
Option A (Divide): Middleware helps allocate or divide resources dynamically to different tasks.
Option B (Combine): Middleware integrates diverse resources into a unified system, making them accessible for parallel computing.
Option C: Correct - middleware is the "glue" that enables both combining and dividing resources seamlessly across distributed nodes.
Option D: Incorrect.
Thus, grid computing middleware both combines and divides resources, making Option C correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Grid and Distributed Computing.

NEW QUESTION # 89
Which of the following is NOT an example of graphical model?

A. Flow charts
B. Electrical circuits
C. Geographical networks
D. Computer networks
E. Road maps

Answer: A

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 # 90
Machine learning can be categorized as:

A. All of the above
B. Reinforcement learning
C. Supervised learning
D. Unsupervised learning

Answer: A

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 # 91
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