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

NEW QUESTION # 16
Which of the following statements is correct?

A. Apache claimed that Spark is able to run parallel jobs 10 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
B. Apache claimed that Spark is able to run parallel jobs 1000 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
C. Apache claimed that Spark is able to run parallel jobs 50 times faster in memory and 5 times faster on disk in comparison to the traditional Hadoop MapReduce
D. Apache claimed that Spark is able to run parallel jobs 100 times faster in memory and 10 times faster on disk in comparison to the traditional Hadoop MapReduce

Answer: D

Explanation:
Apache Spark is a distributed computing framework designed as an improvement over Hadoop's MapReduce.
According to the official Apache Spark documentation:
Spark can run workloads up to 100x faster in memory.
Spark can run workloads up to 10x faster on disk.
This performance gain comes from Spark's use of in-memory computation, DAG execution engine, and optimized query execution, compared to the slower, disk-heavy Hadoop MapReduce framework.
Thus, the correct statement is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Spark vs Hadoop Performance Comparisons.

NEW QUESTION # 17
Which of the following is NOT Structured data?

A. XML
B. None of the above
C. CSV files
D. Relational databases
E. Data tables

Answer: A

Explanation:
Structured data refers to highly organized data stored in predefined formats such as rows and columns.
Relational databases (Option A): Structured.
Data tables (Option B): Structured.
CSV files (Option C): Structured because they are tabular with rows and columns.
XML (Option D): Not structured but semi-structured because it stores hierarchical information with tags and attributes.
Thus, the correct answer is Option D (XML).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Structured vs Semi- structured Data.

NEW QUESTION # 18
Which of the following is the most important part of Hadoop?

A. Spark Framework
B. MapReduce Framework
C. Hadoop Distributed File System (HDFS)
D. Both B and C
E. Both A and B

Answer: E

Explanation:
The Hadoop ecosystem consists of multiple components, but the two core components that define Hadoop are:
HDFS (Hadoop Distributed File System): Provides fault-tolerant, scalable storage across distributed clusters.
It is the backbone for storing massive datasets in a distributed fashion.
MapReduce Framework: Provides the parallel computing and data processing layer in Hadoop, enabling batch analysis over distributed datasets.
Option A: Correct, HDFS is essential.
Option B: Correct, MapReduce is essential.
Option C: Incorrect, Spark is a newer processing framework, but it is not originally part of Hadoop core.
Option D: Correct answer since both HDFS and MapReduce are considered the fundamental parts of Hadoop.
Option E: Incorrect, because Spark is not a core Hadoop component (though it integrates with Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystems: Hadoop Architecture & Components.

NEW QUESTION # 19
The main purpose of a Statement Of Work (SOW) is to get:

A. What the priorities are
B. None of the above
C. All of the above
D. What expectations are realistic
E. Everybody on the same page about what work should be done

Answer: C

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 # 20
What is Scrum?

A. None of the above
B. Scrum and Agile are the same
C. Scrum is a subset of Agile
D. Agile is a subset of Scrum

Answer: C

Explanation:
Scrum is a framework used to implement Agile principles. Agile itself is the overarching philosophy or mindset, while Scrum is one of the most popular frameworks that apply Agile values in practice.
Option A (Correct): Scrum is indeed a subset of Agile. Agile defines the principles (from the Agile Manifesto), and Scrum provides the structure (roles, artifacts, ceremonies).
Option B: Incorrect. Agile is broader and not a subset of Scrum.
Option C: Incorrect. Scrum and Agile are not the same; Agile is the philosophy, Scrum is a methodology under Agile.
Option D: Incorrect because Option A is valid.
Thus, the correct answer is Option A: Scrum is a subset of Agile.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile and Scrum in Data Science Projects.

NEW QUESTION # 21
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