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DASCA Senior Data Scientist Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which of the following is NOT a valid section of Big Data Strategy document?

Answer: D

Explanation:
A Big Data Strategy document provides a framework for aligning data initiatives with organizational objectives. It typically includes:
Business strategy (Option A): Ensures that big data initiatives align with overall corporate strategy.
Key decisions (Option B): Identifies the decisions data will help optimize or automate.
Key business initiatives (Option D): Links big data projects with critical organizational initiatives.
Key business entities (Option E): Defines the core entities (customers, products, channels) around which data will be organized.
However:
Business decisions (Option C): This is redundant and not a standard section; "key decisions" covers this aspect already.
Thus, the correct answer is Option C (Business decisions).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Strategy and Business Alignment.


NEW QUESTION # 45
Which of the following is a trend analysis component of time series decomposition?

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 # 46
Which of the following is NOT an example of the applications of neural networks?

Answer: A

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 # 47
Designing an algorithm to play chess is usually an example of which type of machine learning?

Answer: C

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 # 48
Which of the following is main Machine Learning Library in Python?

Answer: A

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 # 49
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