Get instant Success With DASCA SDS Exam Questions [2026]

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As the tech industry continues to evolve and adapt to new technologies, professionals who hold the Senior Data Scientist (SDS) certification are better equipped to navigate these changes and stay ahead of the curve, increasing their value to employers and clients. In today's fast-paced and ever-changing DASCA sector, having the DASCA SDS Certification has become a necessary requirement for individuals looking to advance their careers and stay competitive in the job market.

DASCA Senior Data Scientist Sample Questions (Q16-Q21):

NEW QUESTION # 16
Business Intelligence (BI) is:

Answer: A

Explanation:
Business Intelligence (BI) is primarily focused on descriptive analytics and reporting - understanding historical and current business performance.
Option A (Descriptive analytics): Correct. BI uses dashboards, reports, and OLAP tools to summarize what has occurred in the past.
Option B ("What happened?"): Correct. BI answers retrospective questions by analyzing transactional and operational data.
Option C (Future state): Incorrect. Predicting future business outcomes falls under predictive analytics or advanced analytics, not BI.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization & BI: Descriptive Analytics and Reporting.


NEW QUESTION # 17
Which of the following is an SLAs specification in case of Internet Service Provider (ISP)?

Answer: B

Explanation:
Service Level Agreements (SLAs) define performance commitments between service providers (like ISPs) and customers. Key SLA metrics include:
MTBF (Option A): Measures reliability by defining the expected average time between service failures.
MTTR (Option B): Measures availability by defining how quickly service can be restored after a failure.
TAT (Option C): Measures responsiveness in resolving customer requests or incidents.
All three are standard SLA performance specifications. Hence, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering and IT Practices: SLAs and Performance Metrics.


NEW QUESTION # 18
Tar is an example of:

Answer: D

Explanation:
TAR (Tape Archive) is a widely used archive file format in Unix/Linux environments. It is used to combine multiple files into a single archive file (with extension .tar).
Option A: Correct. TAR is specifically designed for archiving.
Option B (CSV): Incorrect. CSV (Comma-Separated Values) is a tabular text data format.
Option C (ARV): Incorrect - no such format.
Option D (Text): Incorrect. Though TAR may contain text files, the TAR format itself is not plain text but an archive format.
Option E: Incorrect since Option A is valid.
Thus, TAR is an Archive file format.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Storage Formats in Data Science & Engineering.


NEW QUESTION # 19
Which of the following is a useful feature of functional programming?

Answer: A

Explanation:
Functional programming (FP) is a paradigm widely adopted in data science and big data tools (e.g., Spark with Scala/Python). Its useful features include:
Option A (Higher-Order Functions): Functions can take other functions as arguments or return them, enabling powerful abstractions like map(), reduce(), and filter().
Option B (Immutable Data): Ensures reliability and thread-safety, crucial for distributed computing. Once created, data structures cannot be modified, preventing side effects.
Option C (Lazy Evaluation): Computations are delayed until results are needed, improving performance in large-scale data operations.
Since FP leverages all three features, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Functional Programming in Data Science Tools.


NEW QUESTION # 20
What is Scrumban?

Answer: C

Explanation:
Scrumban is a hybrid Agile methodology that merges Scrum and Kanban to take advantage of the strengths of both.
From Scrum, Scrumban adopts structured sprint planning, roles, and iterative review cycles.
From Kanban, it borrows the visual board system, continuous workflow management, and the pull-based approach, where tasks are pulled into the workflow only when capacity is available.
The pull-based system ensures that teams do not overload themselves and helps manage work-in-progress (WIP) effectively. This makes Scrumban particularly suitable for projects with frequent changes, ongoing maintenance tasks, or teams transitioning from Scrum to Kanban.
Thus, the correct answer is Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Project Management Techniques for Data Science.


NEW QUESTION # 21
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