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Exam DASCA SDS Objectives, Reliable SDS Test Preparation
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DASCA Senior Data Scientist Sample Questions (Q46-Q51):NEW QUESTION # 46
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 changes need to be implemented during the entire process
- D. When clients/stakeholders need to be able to change the scope
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 # 47
Which of the following is NOT a cluster management tool?
- A. Apache Mesos
- B. Apache Hadoop
- C. Zettaset Orchestrator
- D. Apache Ambari
Answer: B
Explanation:
Cluster management tools help in orchestrating and monitoring large-scale distributed computing environments.
Zettaset Orchestrator (A): Commercial tool for Hadoop cluster management.
Apache Mesos (B): A cluster manager that abstracts CPU, memory, and storage to enable fault-tolerant distributed systems.
Apache Ambari (C): An open-source tool for provisioning, managing, and monitoring Hadoop clusters.
Apache Hadoop (D): Not a cluster management tool. Hadoop is a framework for distributed storage and processing (HDFS + MapReduce), not a management tool.
Thus, the correct answer is Option D (Apache Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Hadoop Tools & Cluster Management.
NEW QUESTION # 48
The Big Data Vision Workshop process is ideal for organizations who:
- A. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
- B. Have a desire to leverage Big Data to transform their business but do not know where and how to start
- C. Both A and B
- D. All of the above
- E. Have a wealth of data that they do not know how to monetize
Answer: D
Explanation:
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.
NEW QUESTION # 49
The grid computing environment uses a middleware to:
- A. Combine computing resources
- B. None of the above
- C. Both A and B
- D. Divide computing resources
Answer: C
Explanation:
Grid computing is a distributed computing model where resources (CPU, memory, storage) are pooled across multiple systems to solve large-scale problems.
Option A (Divide): Middleware helps allocate or divide resources dynamically to different tasks.
Option B (Combine): Middleware integrates diverse resources into a unified system, making them accessible for parallel computing.
Option C: Correct - middleware is the "glue" that enables both combining and dividing resources seamlessly across distributed nodes.
Option D: Incorrect.
Thus, grid computing middleware both combines and divides resources, making Option C correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Grid and Distributed Computing.
NEW QUESTION # 50
Which of the following is TRUE about monetization exercise?
- A. Both B and C
- B. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
- C. All of the above
- D. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
- E. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
Answer: C
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 # 51
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