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DASCA Senior Data Scientist Sample Questions (Q71-Q76):

NEW QUESTION # 71
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.

• A. i, ii
• B. ii, iii
• C. All of the above

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 # 72
Which of the following is NOT a process of Use Case?

• A. Brainstorm the questions that the key stakeholders need to answer to facilitate making the decisions
• B. Identify your key business stakeholders
• C. Understand your organization's key business initiatives or business challenge
• D. Brainstorm the outcomes that the key stakeholders need to answer to facilitate making the decisions
• E. Capture the decisions that the key business stakeholders need to make in order to support the organization's key business initiatives

Answer: D

Explanation:
Use Case Development in data science projects involves identifying business needs and mapping analytics to business decisions. The standard steps include:
Option A: Understanding the key initiatives or challenges.
Option B: Identifying the key stakeholders.
Option C: Capturing the decisions stakeholders must make.
Option E: Brainstorming the questions stakeholders need answered to support decisions.
However:
Option D (Brainstorm the outcomes stakeholders need to answer): Incorrect phrasing. It is not "outcomes" that are brainstormed but questions and decisions.
Thus, the correct answer is Option D.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Use Case Development Process.

 

NEW QUESTION # 73
Which of the following is a "thinking like a data scientist" decomposition process?

• A. Strategic Nouns
• B. Business Initiative
• C. All of the above
• D. Business Stakeholder
• E. Both B and C

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 # 74
Which of the following is NOT a correct situation to use Agile?

• A. None of the above
• B. When the final product isn't clearly defined
• C. When clients/stakeholders need to be able to change the scope
• D. When changes need to be implemented during the entire process

Answer: A

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 # 75
Which of the following is TRUE about monetization exercise?

• A. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
• B. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
• C. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
• D. All of the above
• E. Both B and C

Answer: D

Explanation:
The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.
Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.
Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.
Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).
Thus, all three statements are valid, making the correct answer Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Data Monetization Strategies.

 

NEW QUESTION # 76
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