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DASCA Senior Data Scientist Sample Questions (Q16-Q21):

NEW QUESTION # 16
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. Social Network Analysis (SNA) is an example of trend analysis
C. Social Network Analysis (SNA) is an example of graph analysis
D. None of the above
E. SNA is used to investigate social structures and relationships across social networks

Answer: B

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 # 17
Designing an algorithm to play chess is usually an example of which type of machine learning?

A. Reinforcement learning
B. Clustering
C. Supervised learning
D. Pattern density

Answer: A

Explanation:
Chess-playing algorithms are a classic application of Reinforcement Learning (RL) in machine learning.
In RL, an agent (chess program) interacts with an environment (chessboard/game state).
It learns optimal strategies (policies) by trial and error, guided by reward signals (e.g., winning the game, capturing pieces).
Famous examples include DeepMind's AlphaZero and earlier systems like IBM's Deep Blue, which incorporated reinforcement principles along with heuristics.
Option B (Pattern density): Not a recognized ML paradigm.
Option C (Supervised learning): While supervised ML can be used to predict moves from labeled games, chess strategy learning is best modeled as reinforcement learning.
Option D (Clustering): Not applicable; clustering is unsupervised grouping of data.
Thus, chess-playing algorithms are best categorized as Reinforcement Learning # Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Reinforcement Learning Applications: Games & Autonomous Systems.

NEW QUESTION # 18
Which of the following is NOT a correct situation to use Agile?

A. When changes need to be implemented during the entire process
B. When clients/stakeholders need to be able to change the scope
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 # 19
In unsupervised learning, learning takes place by based on these deductions in input data and developing patterns:

A. Detecting irregularities
B. None of the above
C. Both A and B
D. Detecting regularities

Answer: C

Explanation:
Unsupervised learning is a machine learning approach where no labeled outputs are provided. The algorithm discovers patterns or structures directly from raw data.
Option A (Detecting regularities): Correct. Unsupervised learning identifies hidden structures such as clusters, associations, and dimensionality reductions (e.g., k-means clustering, PCA).
Option B (Detecting irregularities): Correct. Outlier detection is also a part of unsupervised learning, often used in anomaly detection (e.g., fraud detection, intrusion detection).
Option C: Correct, since unsupervised learning helps detect both regularities (clusters, groups) and irregularities (outliers, anomalies).
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Unsupervised Learning: Clustering, Anomaly Detection, and Pattern Discovery.

NEW QUESTION # 20
Which of the following is NOT an example of the applications of neural networks?

A. None of the above
B. Character recognition
C. Traveling salesman's problem
D. Stock market prediction
E. Image compression

Answer: C

Explanation:
Neural networks have been widely applied in various domains:
Option A (Character recognition): Correct application - neural networks are highly effective for OCR (Optical Character Recognition).
Option B (Stock market prediction): Correct application - neural networks are used to model time-series and nonlinear patterns in finance.
Option D (Image compression): Correct application - neural nets (autoencoders) are used for dimensionality reduction and compression.
Option C (Traveling salesman's problem): NOT a typical neural network application. This is a combinatorial optimization problem usually solved with heuristics, dynamic programming, or optimization algorithms (not standard neural networks).
Thus, the correct answer is Option C (Traveling salesman's problem).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Applications of Neural Networks.

NEW QUESTION # 21
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