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DASCA Senior Data Scientist Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which of the following architectural techniques is used for parallel processing?

Answer: B

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 # 25
The grid computing environment uses a middleware to:

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 # 26
Which of the following is a Python library for fitting Bayesian networks to real data?

Answer: B

Explanation:
The correct answer isPyMC(Option B).
PyMC is an open-source Python library widely used forBayesian statistical modelingandprobabilistic machine learning. It provides a robust framework for defining and fitting Bayesian networks to real data usingMarkov Chain Monte Carlo (MCMC)sampling techniques, as well asvariational inferencemethods.
This makes it a powerful tool for data scientists who want to work withuncertainty modeling,probabilistic inference, andcausal reasoningin complex datasets.
Let's clarify the other options to avoid confusion:
* Option A: SciLib- There is no standard Python library by this name that is related to Bayesian networks. (It may be confused withSciPyorSciKit-Learn, but those are not specialized for Bayesian inference.)
* Option C: MyLib- This is not a recognized Python package in the data science ecosystem.
* Option D: MCMC- While Markov Chain Monte Carlo is thetechniqueused in Bayesian estimation, it is not a standalone library. Instead, PyMC implements MCMC as part of its computational framework.
* Option E: SCIMC- No such Python library exists; it appears to be a distractor.
PyMC's primary strength is its ability to let data scientists define models in aprobabilistic programming style, making it easier to represent uncertainties and hidden variables in data. This aligns with DASCA's emphasis on ensuring data scientists understand bothstatistical foundationsand thetools required to implement them programmatically.
In practice, PyMC is often used in applications such as:
* Forecasting(e.g., time series with uncertainty bounds)
* Causal inference(estimating hidden relationships in data)
* Risk modeling(finance, healthcare, or supply chain domains)
* Machine learning with uncertainty quantification
Thus,PyMCis the correct library for fitting Bayesian networks in Python.
Reference:DASCA Data Scientist Knowledge Framework (DSKF) -Programming for Data Science & Probabilistic Modeling Tools, Official DASCA Study Guide.


NEW QUESTION # 27
A workflow refers to a:

Answer: B

Explanation:
In data pipelines and process orchestration, a workflow is represented as a Directed Acyclic Graph (DAG):
Directed: Each edge has a direction, representing task dependencies.
Acyclic: No cycles exist; tasks must follow a sequence without looping back.
Graph: Represents tasks as nodes and dependencies as edges.
This structure is common in tools like Apache Airflow, Spark DAGs, and Hadoop MapReduce job schedulers.
Option A & B: Incorrect, as workflows cannot have cycles (would cause infinite loops).
Option D: Incorrect, because workflows are directed, not indirected.
Thus, the correct answer is Option C (Directed Acyclic Graph).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Workflow Management with DAGs.


NEW QUESTION # 28
ElementTree sub-library gives us direct access to:

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 # 29
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