Title: SDS Best Practice, Test SDS Tutorials [Print This Page] Author: tyshaw487 Time: 7 hour before Title: SDS Best Practice, Test SDS Tutorials The DASCA market has become so competitive and challenging with time. To meet this challenge the professionals have to learn new in-demand skills and upgrade their knowledge. With the DASCA SDS certification exam they can do this job quickly and nicely. Your exam preparation with SDS Questions is our top priority at Fast2test. To do this they just enroll in Senior Data Scientist (SDS) certification exam and show some firm commitment and dedication and prepare well to crack the SDS exam.
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Which of the following is TRUE for Tensor?
A. Both B and C
B. Tensor is an array of floating-point numbers
C. In Tensor, there can be arbitrarily many dimensions to the array
D. All of the above
E. Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
Answer: D
Explanation:
A Tensor is a fundamental data structure in modern machine learning frameworks (e.g., TensorFlow, PyTorch). It is best described as a generalization of vectors and matrices to potentially higher dimensions.
Option A: Correct. Tensors typically store numeric values (commonly floating-point numbers) in structured formats.
Option B: Correct. A tensor can have any number of dimensions (rank). For example:
A scalar is a 0-D tensor.
A vector is a 1-D tensor.
A matrix is a 2-D tensor.
Higher-rank tensors can represent images, videos, or multidimensional datasets.
Option C: Correct. Tensors are explicitly designed to allow linear algebra operations, which are the foundation of deep learning computations (matrix multiplications, dot products, etc.).
Therefore, since all three statements are true, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning, Deep Learning Concepts; Official DASCA Study Guide.
NEW QUESTION # 78
Example of amortized performance is:
A. HDFS dictionaries
B. MapReduce dictionaries
C. Python dictionaries
D. All of the above
E. Hadoop dictionaries
Answer: C
Explanation:
Amortized performance refers to averaging the cost of operations over a sequence of actions, ensuring that while some operations may be costly, the overall average time per operation remains efficient.
Python Dictionaries (Option B): Implemented using hash tables. Insertions, deletions, and lookups typically run in O(1) average time, but occasionally require rehashing (costly). The high cost of rehashing is spread over many operations, giving amortized constant-time performance.
Option A (Hadoop dictionaries): Not standard terminology.
Option C (HDFS dictionaries): HDFS doesn't use dictionary structures in this sense.
Option D (MapReduce dictionaries): MapReduce uses key-value pairs, but amortized dictionary performance is not its focus.
Thus, the correct answer is Option B (Python dictionaries).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Hash Tables & Amortized Analysis.
NEW QUESTION # 79
A burn down chart shows:
A. The number of hours worked after dark
B. The rate of reduction of budget for a project
C. The declining energy of the team
D. The volume of work and features completed
Answer: D
Explanation:
A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.
Option A: Incorrect. Burn down charts don't measure team "energy" or motivation levels.
Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.
Option C: Incorrect. Hours worked after dark is irrelevant.
Option D: Incorrect. Budget reduction is not tracked in burn down charts.
Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Project Management & Agile Tools in Data Science.
NEW QUESTION # 80
The Big Data Vision Workshop process is ideal for organizations who:
A. 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
B. Have a wealth of data that they do not know how to monetize
C. Both A and B
D. All of the above
E. Have a desire to leverage Big Data to transform their business but do not know where and how to start
Answer: D
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 # 81
Which of the following is a DevOps Practice?
A. Continuous integration
B. Continuous delivery
C. Continuous build
D. All of the above
Answer: D
Explanation:
DevOps is a collaborative practice that integrates software development (Dev) and IT operations (Ops) to shorten development cycles and deliver applications reliably. Common DevOps practices include:
Continuous Build (Option A): Automating compilation and packaging of source code to ensure consistent builds.
Continuous Integration (Option B): Developers frequently merge code into a shared repository, which is automatically tested to catch integration issues early.
Continuous Delivery (Option C): Automating software release pipelines so applications can be deployed to production quickly and reliably.
Since all of these are essential DevOps practices, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: DevOps Practices in Data Science Projects.
NEW QUESTION # 82
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