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SDS Test Question | SDS Exam Assessment

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DASCA Senior Data Scientist Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which of the following is TRUE for "By" analysis?

A. The "By" analysis technique reinforces the process of "thinking like a data scientist."
B. Both B and C
C. "By" analysis is a technique by which business subject matter experts (SMEs) and the Data Science team could collaborate to uncover new variables and metrics that might be better predictors of business performance.
D. All of the above
E. "By" analysis is used to create a collaborative technique to drive alignment between the business users and the data scientists to identify and brainstorm variables and metrics that might be better predictors of business performance.

Answer: D

Explanation:
"By" analysis is one of the foundational approaches recommended in the DASCA Data Scientist Knowledge Framework for structuring problem-solving in data science. The purpose of "By" analysis is to enable data scientists and business stakeholders to think beyond obvious data correlations and uncover deeper drivers of business outcomes.
At its core, the technique reinforces the discipline ofthinking like a data scientist(Option A). This involves reframing business questions into analytical structures and asking "What drives this metricbywhich factors?" For example, customer churn might be analyzedbydemographics, purchase behavior, or service usage. This structured mindset is critical for ensuring scientific rigor in business problem analysis.
In addition, "By" analysis emphasizes collaboration betweenSubject Matter Experts (SMEs)andData Science teams(Option B). SMEs bring contextual domain knowledge, while data scientists bring analytical and statistical expertise. Together, they brainstorm possible explanatory variables or metrics that could become strong predictors of business performance.
Furthermore, the process provides acollaborative bridgebetween business and technical stakeholders (Option C). It ensures that the exploration of data is not isolated in silos but is grounded in both domain insights and advanced analytical methods. This alignment is crucial for building models that are not only technically sound but also relevant and actionable in real-world business contexts.
Since Options A, B, and C are correct and complementary, the best choice isOption E: All of the above.
Reference:DASCA Data Scientist Knowledge Framework (DSKF) -Data Science Process Fundamentals & Collaborative Analysis Techniques(Official DASCA Study & Exam Preparation Guide).

NEW QUESTION # 45
Which of the following architectural techniques is used for parallel processing?

A. Very Long Instruction Words (VLIW) Technique
B. The Superscalar Technique
C. The SuperVector Technique
D. Both A and B
E. Both B and C

Answer: D

Explanation:
Parallel processing architectures are designed to execute multiple instructions or operations simultaneously:
Superscalar Technique (Option A): Uses multiple execution units so that several instructions can be issued and executed in parallel within a single CPU cycle.
VLIW Technique (Option B): Uses very long instruction words, where multiple operations are encoded into a single instruction and executed in parallel.
SuperVector (Option C): Refers to vector processors, which process large arrays of data but is not classified as a mainstream architectural parallel technique in modern CPU design.
Therefore, the primary architectural techniques for parallel processing are Superscalar and VLIW, making Option D (Both A and B) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Parallel and Distributed Processing.

NEW QUESTION # 46
Which of these statements reflects a null hypothesis?

A. Women will score higher than men on empathy
B. Men will score higher than women on spatial awareness
C. There will be no relationship between caffeine consumption and performance
D. As temperature increases, so too will the level of aggression
E. There will be a significant difference between group 1 and group 2

Answer: C

Explanation:
A null hypothesis (H#) is the default assumption in statistical testing that there is no effect, no difference, or no relationship between variables.
Option A: Correct. This states explicitly that there is no relationship between caffeine consumption and performance # fits the definition of a null hypothesis.
Options B, C, D, E: These all hypothesize differences or relationships # they are examples of alternative hypotheses (H#), not null.
Thus, the correct answer is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistics in Data Science: Hypotheses, Errors, and Testing.

NEW QUESTION # 47
Spark is written in:

A. C
B. Scala
C. C++
D. Python
E. Java

Answer: B

Explanation:
Apache Spark is an open-source distributed computing framework widely used for big data processing and machine learning pipelines.
The core implementation of Spark is written in Scala (Option A), which runs on the JVM (Java Virtual Machine).
Spark also provides APIs for Java, Python (PySpark), R, and SQL, but its native language is Scala.
Options C (C) and D (C++) are incorrect; Spark is not written in these languages.
Python (Option E) is a supported API, but Spark itself is not written in Python.
Thus, the correct answer is Scala (Option A).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools for Big Data & Distributed Computing.

NEW QUESTION # 48
Which of the following is NOT Structured data?

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

Answer: D

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 # 49
......

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