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DASCA Senior Data Scientist Sample Questions (Q70-Q75):
NEW QUESTION # 70
The grid computing environment uses a middleware to:
- A. Divide computing resources
- B. Both A and B
- C. Combine computing resources
- D. None of the above
Answer: B
Explanation:
Grid computing is a distributed computing model where resources (CPU, memory, storage) are pooled across multiple systems to solve large-scale problems.
Option A (Divide): Middleware helps allocate or divide resources dynamically to different tasks.
Option B (Combine): Middleware integrates diverse resources into a unified system, making them accessible for parallel computing.
Option C: Correct - middleware is the "glue" that enables both combining and dividing resources seamlessly across distributed nodes.
Option D: Incorrect.
Thus, grid computing middleware both combines and divides resources, making Option C correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Grid and Distributed Computing.
NEW QUESTION # 71
Which of the following is TRUE for "By" analysis?
- A. The "By" analysis technique reinforces the process of "thinking like a data scientist."
- B. "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.
- C. "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.
- D. All of the above
- E. Both B and C
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 # 72
Which of the following is a trend analysis component of time series decomposition?
- A. Cyclical
- B. Seasonal
- C. Irregular
- D. All of the above
- E. Both A and B
Answer: D
Explanation:
Time series decomposition breaks down data into components to better understand underlying patterns and support forecasting. The main components are:
Trend: Long-term progression (upward or downward).
Seasonal: Repeating short-term patterns (e.g., monthly or quarterly).
Cyclical (Option A): Medium- to long-term cycles (e.g., business cycles).
Irregular/Residual (Option C): Random, unpredictable variations.
Since trend analysis involves examining cyclical, seasonal, and irregular components, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Time Series Decomposition and Trend Analysis.
NEW QUESTION # 73
Which of the following is an example of NLP?
- A. Finding which text documents are about similar topics
- B. Using Twitter to assess public sentiment
- C. All of the above
- D. Flagging e-mails as spam
- E. Both A and C
Answer: C
Explanation:
Natural Language Processing (NLP) is the field of machine learning that deals with human language understanding and generation. Applications include:
Option A (Spam detection): NLP techniques classify emails based on text patterns and context.
Option B (Sentiment analysis on Twitter): NLP models analyze textual data to extract emotions, opinions, and trends.
Option C (Topic modeling): NLP clustering and probabilistic models (e.g., LDA) classify documents by semantic similarity.
Since all are valid NLP applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: NLP in Real- World Use Cases.
NEW QUESTION # 74
Which of the following is NOT a main data container in Python?
- A. Tuples
- B. LinkedList
- C. Lists
- D. Dict
Answer: B
Explanation:
Python's built-in data containers are widely used in data science and programming:
Lists (Option A): Ordered, mutable sequences.
Tuples (Option B): Ordered, immutable sequences.
Dict (Option D): Dictionaries, key-value data structures implemented using hash tables.
LinkedList (Option C): Python does not provide a native linked list as a built-in container. While linked lists can be implemented manually or through external libraries (e.g., collections.deque for efficient appends
/pops), they are not part of Python's main native containers.
Thus, the correct answer is Option C (LinkedList).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Python Data Structures.
NEW QUESTION # 75
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