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Latest SDS Braindumps Questions | SDS Valid Exam Materials

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DASCA Senior Data Scientist Sample Questions (Q41-Q46):

NEW QUESTION # 41
Which of the following is an example of graphical model?

Answer: D

Explanation:
Graphical models are probabilistic models that represent variables and dependencies using graphs:
Markov Random Fields (Option A): Undirected graphical models that capture joint distributions over variables with neighborhood dependencies.
Bayesian Networks (Option B): Directed acyclic graphical models that encode conditional dependencies between random variables.
Geographical Networks (Option C): While they are graphs, they are not probabilistic graphical models used in statistics/ML.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graphical Models (Bayesian Networks
& Markov Random Fields).


NEW QUESTION # 42
Which of the following is an example of NLP?

Answer: C

Explanation:
Natural Language Processing (NLP) is the field of machine learning that deals with human language understanding and generation. Applications include:
Option A (Spam detection): NLP techniques classify emails based on text patterns and context.
Option B (Sentiment analysis on Twitter): NLP models analyze textual data to extract emotions, opinions, and trends.
Option C (Topic modeling): NLP clustering and probabilistic models (e.g., LDA) classify documents by semantic similarity.
Since all are valid NLP applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: NLP in Real- World Use Cases.


NEW QUESTION # 43
Designing an algorithm to play chess is usually an example of which type of machine learning?

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 # 44
Which of the following errors refers to the wrong negation of a true null hypothesis?

Answer: E

Explanation:
In hypothesis testing, two main types of errors are defined:
Type I Error (Option A): Occurs when the null hypothesis (H#) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.
Type II Error (Option B): Occurs when the null hypothesis (H#) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.
Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.
Thus, the "wrong negation of a true null hypothesis" refers to a Type I Error (false positive).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Foundations in Data Science: Hypothesis Testing & Errors.


NEW QUESTION # 45
Which of the following is TRUE for Chief Data Monetization Officer (CDMO)?
i. CDMO should focus on driving and deriving value from the organization's data and analytic assets.
ii. The CDMO should own the organization's investment decisions with respect to data and analytics.
iii. CDMO should have revenue and margin responsibilities.

Answer: C

Explanation:
The Chief Data Monetization Officer (CDMO) is a C-level executive role emerging in data-driven organizations. Their focus is on turning data into business value and revenue growth.
Statement i: Correct. The CDMO is responsible for value realization from data and analytics investments.
Statement ii: Correct. The CDMO owns decisions around investment prioritization for data initiatives.
Statement iii: Correct. Since monetization is directly linked to business performance, the CDMO must be accountable for revenue and margin impact.
Hence, all three statements are correct, making the right answer Option C (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Leadership & Monetization Roles.


NEW QUESTION # 46
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