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Three High in Demand DASCA SDS Exam Questions Formats

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Three High in Demand DASCA SDS Exam Questions Formats

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DASCA Senior Data Scientist Sample Questions (Q43-Q48):NEW QUESTION # 43
ElementTree sub-library gives us direct access to:
  • A. Parse tree of the XML
  • B. Copy tree of the XML
  • C. Delete tree of the XML
  • D. None of the above
  • E. Insert tree of the XML
Answer: A
Explanation:
In Python, the ElementTree module (part of the standard library xml.etree.ElementTree) provides a simple and efficient API for parsing and creating XML data.
The main feature of ElementTree is its ability to provide direct access to the parse tree of an XML document.
This allows developers to:
Parse XML into an in-memory tree structure.
Traverse, search, modify, and extract information from XML elements.
Write back changes into XML files.
Options B, C, and D (Delete, Copy, Insert tree) are not standard terminology in XML handling with ElementTree. While you can delete, insert, or copy elements, the module itself primarily gives parse tree access.
Thus, the correct answer is Option A (Parse tree of the XML).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: XML/JSON Handling in Python.

NEW QUESTION # 44
Which of the following can be classified as factor analysis in machine learning?
  • A. Exploratory factor analysis
  • B. Confirmatory factor analysis
  • C. None of the above
  • D. Both A and B
Answer: D
Explanation:
Factor analysis is a dimensionality reduction technique used to uncover latent variables (factors) that explain observed patterns of correlations in data. It is widely used in psychometrics, social sciences, and machine learning.
Exploratory Factor Analysis (EFA, Option A): Used when the underlying factor structure is unknown, aiming to discover potential latent variables.
Confirmatory Factor Analysis (CFA, Option B): Used when there is a hypothesis about factor structure, and the goal is to confirm it statistically.
Both are valid approaches to factor analysis, hence the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Dimensionality Reduction & Factor Analysis in Machine Learning.

NEW QUESTION # 45
Which of the following is True about Time Series Analysis?
  • A. Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through
  • B. Both A and B
  • C. All of the above
  • D. Projecting the value of the time series at future points in time, such as a stock whose price we want to predict
  • E. Predicting when/whether an event will occur, such as a failure of the machine generating the data
Answer: C
Explanation:
Time Series Analysis (TSA) is the process of analyzing data collected sequentially over time to extract meaningful insights.Applications include:
Option A: Correct. Event prediction (e.g., failure detection in IoT or predictive maintenance).
Option B: Correct. Forecasting future values (e.g., stock price, sales forecasting).
Option C: Correct. Pattern discovery in large-scale time series datasets using clustering, anomaly detection, or seasonality detection.
Since all three are true, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Time Series Analysis and Forecasting.

NEW QUESTION # 46
The Big Data Vision Workshop process is ideal for organizations who:
  • A. Have a wealth of data that they do not know how to monetize
  • B. Both A and B
  • C. All of the above
  • D. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
  • E. Have a desire to leverage Big Data to transform their business but do not know where and how to start
Answer: C
Explanation:
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.

NEW QUESTION # 47
In regression, the principle of machine learning is used to optimize the parameters to:
  • A. Minimize the approximation error
  • B. Calculate the closest possible outcomes
  • C. None of the above
  • D. Both A and B
Answer: D
Explanation:
Regression is a supervised learning technique where a model estimates the relationship between input features (independent variables) and an output (dependent variable).
Option A: Correct. The learning process involves optimizing model parameters (e.g., coefficients in linear regression) to minimize approximation error. Common loss functions include Mean Squared Error (MSE) or Mean Absolute Error (MAE).
Option B: Correct. Minimizing error enables the model to produce the closest possible outcomes to the actual observed values, ensuring accurate predictions.
Option C: Correct, since both A and B are true.
Option D: Incorrect.
Thus, regression optimization in machine learning aims to minimize approximation error and generate closest possible outcomes, making Option C the correct answer.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics & Machine Learning: Regression Models and Optimization Principles.

NEW QUESTION # 48
......
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