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DASCA Senior Data Scientist Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which of the following is NOT a part of Internal Process Optimization?

A. Business Insights
B. Business Metamorphosis
C. None of the above
D. Business Monitoring
E. Business Optimization

Answer: B

Explanation:
Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.
Typical components of IPO include:
Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.
Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.
Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.
However:
Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.
Therefore, the correct answer is Option B (Business Metamorphosis).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Internal Process Optimization.

NEW QUESTION # 25
The aim of developing Scribe was to offer reliability with using:

A. Complex protocols
B. Extensive protocols
C. High disk space usage
D. None of the above

Answer: D

Explanation:
Scribe is a server developed by Facebook for aggregating log data streamed in real time from many servers.
Its primary aim was reliability and simplicity in collecting logs at scale.
Unlike other systems, Scribe was designed to avoid complex or extensive protocols and to ensure lightweight log transfer.
It also avoids unnecessary high disk usage, relying instead on efficient pipelines.
Thus, the correct answer is Option D (None of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Tools: Log Collection Systems (Scribe, Kafka, Flume).

NEW QUESTION # 26
Which of the following is NOT a valid section of Big Data Strategy document?

A. Key business initiatives
B. Key business entities
C. Key decisions
D. Business strategy
E. Business decisions

Answer: E

Explanation:
A Big Data Strategy document provides a framework for aligning data initiatives with organizational objectives. It typically includes:
Business strategy (Option A): Ensures that big data initiatives align with overall corporate strategy.
Key decisions (Option B): Identifies the decisions data will help optimize or automate.
Key business initiatives (Option D): Links big data projects with critical organizational initiatives.
Key business entities (Option E): Defines the core entities (customers, products, channels) around which data will be organized.
However:
Business decisions (Option C): This is redundant and not a standard section; "key decisions" covers this aspect already.
Thus, the correct answer is Option C (Business decisions).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Strategy and Business Alignment.

NEW QUESTION # 27
Which of the following is NOT a correct situation to use Agile?

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

Answer: D

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 # 28
What is TRUE for "rehashing"?

A. Both A and B
B. Key/value pairs from the original table can be inserted into the new, larger one
C. It requires a new hash function, which maps values into a larger range of integers
D. Allocate a new, larger hash table in memory
E. All of the above

Answer: E

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 # 29
......

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