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[General] SDS Study Tool & Pdf SDS Files

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【General】 SDS Study Tool & Pdf SDS Files

Posted at yesterday 13:25      View：6 | Replies：0        Print      Only Author   [Copy Link] 1#
P.S. Free & New SDS dumps are available on Google Drive shared by Pass4Leader: https://drive.google.com/open?id=1Y5ZP2E0YK8iS9fV2K1EVwGDEvKe_1WwI
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DASCA Senior Data Scientist Sample Questions (Q35-Q40):NEW QUESTION # 35
Which of the following is CORRECT?
i. CDMO should NOT own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
ii. CDMO should own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
iii. CIO should NOT own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
iv. CIO should own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
  • A. i, iii
  • B. ii, iii
  • C. i, iv
  • D. ii, iv
Answer: C
Explanation:
The Chief Data Monetization Officer (CDMO) is responsible for driving value extraction from data assets, not owning infrastructure. By contrast, the Chief Information Officer (CIO) traditionally owns technology infrastructure, including data lakes, warehouses, and data architectures.
Statement i: Correct. CDMO should NOT own data infrastructure. Their role is strategic - monetizing insights, not managing systems.
Statement ii: Incorrect. CDMO focuses on monetization, not architecture ownership.
Statement iii: Incorrect. CIO indeed does own these systems.
Statement iv: Correct. CIO owns the data lake, data warehouse, and underlying architectures/technologies.
Thus, the correct combination is i and iv # Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Roles in Data Leadership: CIO vs CDMO.

NEW QUESTION # 36
The main purpose of a Statement Of Work (SOW) is to get:
  • A. Everybody on the same page about what work should be done
  • B. All of the above
  • C. What the priorities are
  • D. None of the above
  • E. What expectations are realistic
Answer: B
Explanation:
A Statement of Work (SOW) is a formal document that defines the scope, objectives, deliverables, timeline, and expectations of a project. In data science and IT projects, it ensures:
Clarity of scope (Option A): Everyone understands exactly what work should be done.
Clear priorities (Option B): It defines what is most critical for success.
Realistic expectations (Option C): It aligns stakeholders by setting measurable and achievable goals.
Since all of these are essential purposes of an SOW, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications: Project Governance and SOW.

NEW QUESTION # 37
Which of the following is NOT a correct situation to use Agile?
  • A. None of the above
  • B. When changes need to be implemented during the entire process
  • C. When the final product isn't clearly defined
  • 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 # 38
The aim of developing Scribe was to offer reliability with using:
  • A. High disk space usage
  • B. Extensive protocols
  • C. None of the above
  • D. Complex protocols
Answer: C
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 # 39
Spark should be used when:
  • A. None of the above
  • B. Both A and B
  • C. Data is not massive
  • D. Data is massive
Answer: D
Explanation:
Apache Spark is a distributed data processing engine optimized for big data scenarios. It is specifically designed to handle:
Large-scale datasets spread across clusters.
Massive streaming or batch data pipelines.
Machine learning and graph processing at scale.
Option A: Correct - Spark excels when data is massive and distributed.
Option B: Incorrect - Spark is overkill for small data (Pandas, NumPy, or scikit-learn would be more efficient).
Option C: Incorrect - Spark is not optimized for small datasets.
Option D: Incorrect - since A is valid.
Thus, Spark should be used when data is massive # Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Processing: Apache Spark Applications.

NEW QUESTION # 40
......
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