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DASCA Senior Data Scientist Sample Questions (Q81-Q86):

NEW QUESTION # 81
Which of these statements reflects a null hypothesis?

Answer: C

Explanation:
A null hypothesis (H#) is the default assumption in statistical testing that there is no effect, no difference, or no relationship between variables.
Option A: Correct. This states explicitly that there is no relationship between caffeine consumption and performance # fits the definition of a null hypothesis.
Options B, C, D, E: These all hypothesize differences or relationships # they are examples of alternative hypotheses (H#), not null.
Thus, the correct answer is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistics in Data Science: Hypotheses, Errors, and Testing.


NEW QUESTION # 82
Which of the following is a "thinking like a data scientist" decomposition process?

Answer: C

Explanation:
The "Thinking Like a Data Scientist" (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:
Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).
Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.
Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.
Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Science Fundamentals: Thinking Like a Data Scientist Process.


NEW QUESTION # 83
What is the agenda of discussion at a "stand up" meeting of an Agile team?

Answer: E

Explanation:
A daily stand-up meeting (also called a daily Scrum) is a short meeting (usually 15 minutes) that Agile teams hold to synchronize progress. Its agenda is structured around three key questions:
What was accomplished yesterday? (Progress review).
What is planned for today? (Work alignment).
What impediments or roadblocks exist? (Barriers identification).
This process enhances transparency, communication, and accountability, ensuring the team can quickly address obstacles and stay aligned with sprint goals.
Option A: Correct - yesterday's work is discussed.
Option B: Correct - today's planned tasks are outlined.
Option C: Correct - roadblocks are highlighted.
Option D: Incomplete since it misses C.
Option E: Correct - covers all agenda items.
Thus, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Practices in Data Science Projects.


NEW QUESTION # 84
Which of the following is TRUE for Business Metamorphosis?

Answer: E

Explanation:
Business Metamorphosis is the most advanced phase in the Big Data Business Model Maturity Index (BDBMMI), where organizations fundamentally transform their business models through analytics-driven insights.
Option A: Correct. This phase helps organizations identify big data requirements related to decisions, analytics, and sources that drive business transformation.
Option B: Correct. Organizations integrate customer usage patterns, product behaviors, and market trends into their decision-making to redesign or innovate their business model.
Option C: Correct. Business Metamorphosis ensures that the core business model evolves continuously, guided by insights derived across maturity stages.
Since all are correct, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Business Model Maturity Index.


NEW QUESTION # 85
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 # 86
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