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DASCA Senior Data Scientist Sample Questions (Q41-Q46):

NEW QUESTION # 41
Spark programs can be written in:

A. All of the above
B. Scala
C. Python
D. Java
E. None of the above

Answer: A

Explanation:
Apache Spark supports multiple programming languages for developing distributed applications:
Java (Option A): Supported through Spark's JVM-based APIs.
Scala (Option B): Spark is natively written in Scala, and Scala APIs provide full functionality.
Python (Option C): Supported via PySpark, enabling Python developers to leverage Spark.
Additionally, Spark also supports R and SQL-like queries, making it versatile for data scientists and engineers.
Thus, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools: Spark APIs for Java, Scala, Python, and R.

NEW QUESTION # 42
Which of the following is correct?
i. LaTeX is used to publish work in a scientific journal
ii. LaTeX is a markup language that can be compiled into formatted documents iii. LaTeX is for publishing scientific papers

A. ii, iii
B. i, iii
C. i, ii
D. i, ii, iii

Answer: D

Explanation:
LaTeX is a high-quality typesetting system widely used in academia, particularly in scientific publishing.
Statement i: Correct. LaTeX is widely used to prepare manuscripts for scientific journals, theses, and technical reports.
Statement ii: Correct. LaTeX is a markup language (similar to HTML in concept) that compiles into formatted PDFs/documents.
Statement iii: Correct. LaTeX is a standard for publishing scientific papers due to its ability to handle complex mathematical equations, references, and formatting.
Thus, all three statements are true # Option B (i, ii, iii).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools for Data Science: LaTeX for Scientific Documentation.

NEW QUESTION # 43
Machine learning can be categorized as:

A. Reinforcement learning
B. All of the above
C. Supervised learning
D. Unsupervised learning

Answer: B

Explanation:
Machine learning (ML) can be broadly divided into three main paradigms:
Supervised Learning (Option A):
Data includes labeled outputs (e.g., classification, regression).
Goal: Learn a mapping from input to output.
Unsupervised Learning (Option B):
Data has no labels.
Goal: Discover hidden patterns (e.g., clustering, dimensionality reduction).
Reinforcement Learning (Option C):
Agent interacts with an environment and learns by maximizing cumulative rewards through trial and error.
Used in robotics, game AI, and autonomous systems.
Since all three categories are valid, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Paradigms: Supervised, Unsupervised, Reinforcement.

NEW QUESTION # 44
What is the agenda of discussion at a "stand up" meeting of an Agile team?

A. Both A and B
B. All of the above
C. What they accomplished the previous day
D. Any roadblocks they are running into
E. What they are planning to do today

Answer: B

Explanation:
A daily stand-up meeting (also called a daily Scrum) is a short meeting (usually 15 minutes) that Agile teams hold to synchronize progress. Its agenda is structured around three key questions:
What was accomplished yesterday? (Progress review).
What is planned for today? (Work alignment).
What impediments or roadblocks exist? (Barriers identification).
This process enhances transparency, communication, and accountability, ensuring the team can quickly address obstacles and stay aligned with sprint goals.
Option A: Correct - yesterday's work is discussed.
Option B: Correct - today's planned tasks are outlined.
Option C: Correct - roadblocks are highlighted.
Option D: Incomplete since it misses C.
Option E: Correct - covers all agenda items.
Thus, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Practices in Data Science Projects.

NEW QUESTION # 45
Exploratory analytic algorithms help the Data Science team to better:

A. Both A and B
B. All of the above
C. Understand patterns in the data
D. Understand the data content
E. Gain a high-level understanding of relationships

Answer: B

Explanation:
Exploratory analytics (often referred to as Exploratory Data Analysis - EDA) is a fundamental step in data science, enabling practitioners to discover initial insights, detect anomalies, and understand the structure of datasets before applying predictive or prescriptive modeling.
Option A (Understand the data content): Correct. EDA techniques (descriptive statistics, summary tables, profiling) reveal missing values, data types, and distributions.
Option B (Gain a high-level understanding of relationships): Correct. Correlation analysis, scatter plots, and cross-tabulations help identify dependencies between variables.
Option C (Understand patterns in the data): Correct. Visualization and clustering methods help discover hidden structures, seasonalities, and outliers.
Since exploratory algorithms contribute to all of these objectives, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Exploratory Analytics & EDA.

NEW QUESTION # 46
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