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DASCA Senior Data Scientist Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which of the following is NOT a part of Internal Process Optimization?

A. None of the above
B. Business Metamorphosis
C. Business Optimization
D. Business Monitoring
E. Business Insights

Answer: B

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 # 22
Which of the following is the common evolutionary algorithm of neural networks?

A. Evolution strategy
B. Genetic algorithm
C. Genetic programming
D. All of the above

Answer: D

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 # 23
Which of the following is NOT an example of graphical model?

A. Flow charts
B. Electrical circuits
C. Computer networks
D. Geographical 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 # 24
Bernoulli random variable is a type of:

A. Discrete random variable
B. Both A and B
C. Continuous random variable
D. Sometimes Discrete or sometimes Continuous random variable

Answer: A

Explanation:
A Bernoulli random variable is the simplest form of discrete random variable.
It can take only two values:
1 with probability p (success).
0 with probability (1 - p) (failure).
Since the outcomes are finite (binary), it is a discrete random variable.
Option B (Continuous): Incorrect, as continuous variables take values from an interval (e.g., real numbers).
Option C and D: Incorrect, as Bernoulli is always discrete, never continuous.
Thus, the correct answer is Option A (Discrete random variable).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Probability & Statistics for Data Science: Random Variables and Bernoulli Distribution.

NEW QUESTION # 25
Which of the following is FALSE for Social Network Analysis (SNA)?

A. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
B. None of the above
C. Social Network Analysis (SNA) is an example of graph analysis
D. Social Network Analysis (SNA) is an example of trend analysis
E. SNA is used to investigate social structures and relationships across social networks

Answer: D

Explanation:
Social Network Analysis (SNA) is a powerful analytical method that applies graph theory to study relationships among entities (people, organizations, computers, etc.).
Option A: Correct. SNA is indeed an example of graph analysis because it models entities as nodes and their relationships as edges/ties.
Option B: FALSE. SNA is not an example of trend analysis. Trend analysis focuses on temporal patterns (time series), while SNA is structural and relational.
Option C: Correct. SNA investigates structures such as communities, influencers, and information diffusion in networks.
Option D: Correct. The characterization of nodes and edges is central to SNA.
Option E: Incorrect, since we've identified Option B as false.
Thus, the false statement is Option B.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graph Analysis & Social Network Analysis.

NEW QUESTION # 26
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