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DASCA Senior Data Scientist Sample Questions (Q46-Q51):

NEW QUESTION # 46
The Big Data Vision Workshop process is ideal for organizations who:

Answer: E

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 # 47
Example of amortized performance is:

Answer: E

Explanation:
Amortized performance refers to averaging the cost of operations over a sequence of actions, ensuring that while some operations may be costly, the overall average time per operation remains efficient.
Python Dictionaries (Option B): Implemented using hash tables. Insertions, deletions, and lookups typically run in O(1) average time, but occasionally require rehashing (costly). The high cost of rehashing is spread over many operations, giving amortized constant-time performance.
Option A (Hadoop dictionaries): Not standard terminology.
Option C (HDFS dictionaries): HDFS doesn't use dictionary structures in this sense.
Option D (MapReduce dictionaries): MapReduce uses key-value pairs, but amortized dictionary performance is not its focus.
Thus, the correct answer is Option B (Python dictionaries).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Hash Tables & Amortized Analysis.


NEW QUESTION # 48
Which of the following is correct about customer lifetime value (CLTV)?
i. Most organizations determine the current customer lifetime value (CLTV) based on historic sales over past
12 to 18 months
ii. The goal of the CLTV score is to help marketing and store personnel to determine the "value" of a customer

Answer: C

Explanation:
Customer Lifetime Value (CLTV) is a predictive metric estimating the total revenue a business can reasonably expect from a customer during their entire relationship.
Statement i: Correct. Many organizations calculate CLTV using historic transactional data, often looking at sales records over the past 12-18 months to establish baselines.
Statement ii: Correct. The primary purpose of CLTV is to help marketing, sales, and retail teams understand customer value, enabling them to allocate budgets effectively for retention, promotions, and personalized marketing.
Thus, both statements are correct # Option C (Both i and ii).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: CLTV Metrics and Marketing Analytics.


NEW QUESTION # 49
Which of the following is correct?

Answer: E

Explanation:
Pandas is one of the most widely used Python libraries for data analysis and manipulation. Its central object is the DataFrame.
Option A: Correct. DataFrame is the core data structure in Pandas.
Option B: Correct. DataFrame resembles SQL tables and R data frames, supporting row/column indexing, joins, and grouping.
Option C: Correct. A DataFrame is essentially a 2D labeled table consisting of rows and columns.
Option D: Correct, but not fully inclusive.
Option E: Correct, since all of A, B, and C are true.
Thus, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Pandas Data Structures.


NEW QUESTION # 50
A burn down chart shows:

Answer: C

Explanation:
A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.
Option A: Incorrect. Burn down charts don't measure team "energy" or motivation levels.
Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.
Option C: Incorrect. Hours worked after dark is irrelevant.
Option D: Incorrect. Budget reduction is not tracked in burn down charts.
Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Project Management & Agile Tools in Data Science.


NEW QUESTION # 51
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