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DASCA Senior Data Scientist Sample Questions (Q71-Q76):
NEW QUESTION # 71
Which of the following is TRUE about monetization exercise?
- A. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
- B. All of the above
- C. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
- 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. Both B and C
Answer: B
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 # 72
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. All of the above
- C. Both A and B
- D. Have a wealth of data that they do not know how to monetize
- E. Have a desire to leverage Big Data to transform their business but do not know where and how to start
Answer: B
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 # 73
What is TRUE for "rehashing"?
- A. Key/value pairs from the original table can be inserted into the new, larger one
- B. It requires a new hash function, which maps values into a larger range of integers
- C. Allocate a new, larger hash table in memory
- D. All of the above
- E. Both A and B
Answer: D
Explanation:
Rehashing is a technique used in dynamic hash tables when the load factor (ratio of entries to bucket size) exceeds a certain threshold. It ensures efficient lookup, insertion, and deletion operations.
Option A (Correct): A larger hash table is allocated in memory to accommodate more entries.
Option B (Correct): A new hash function is typically required to map keys into the expanded table range.
Option C (Correct): All key-value pairs from the old table are re-inserted (rehashed) into the new table using the new hash function.
Since all three conditions (A, B, and C) are true, the best choice is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Data Structures & Hashing Techniques
NEW QUESTION # 74
Which of the following is FALSE for Social Network Analysis (SNA)?
- A. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
- B. None of the above
- C. Social Network Analysis (SNA) is an example of graph analysis
- D. Social Network Analysis (SNA) is an example of trend analysis
- E. SNA is used to investigate social structures and relationships across social networks
Answer: D
Explanation:
Social Network Analysis (SNA) is a powerful analytical method that applies graph theory to study relationships among entities (people, organizations, computers, etc.).
Option A: Correct. SNA is indeed an example of graph analysis because it models entities as nodes and their relationships as edges/ties.
Option B: FALSE. SNA is not an example of trend analysis. Trend analysis focuses on temporal patterns (time series), while SNA is structural and relational.
Option C: Correct. SNA investigates structures such as communities, influencers, and information diffusion in networks.
Option D: Correct. The characterization of nodes and edges is central to SNA.
Option E: Incorrect, since we've identified Option B as false.
Thus, the false statement is Option B.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graph Analysis & Social Network Analysis.
NEW QUESTION # 75
Which of the following is NOT a main data container in Python?
- A. LinkedList
- B. Dict
- C. Lists
- D. Tuples
Answer: A
Explanation:
Python's built-in data containers are widely used in data science and programming:
Lists (Option A): Ordered, mutable sequences.
Tuples (Option B): Ordered, immutable sequences.
Dict (Option D): Dictionaries, key-value data structures implemented using hash tables.
LinkedList (Option C): Python does not provide a native linked list as a built-in container. While linked lists can be implemented manually or through external libraries (e.g., collections.deque for efficient appends
/pops), they are not part of Python's main native containers.
Thus, the correct answer is Option C (LinkedList).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Python Data Structures.
NEW QUESTION # 76
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