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DASCA Senior Data Scientist Sample Questions (Q59-Q64):
NEW QUESTION # 59
Which of the following is a "thinking like a data scientist" decomposition process?
- A. All of the above
- B. Strategic Nouns
- C. Both B and C
- D. Business Stakeholder
- E. Business Initiative
Answer: A
Explanation:
The "Thinking Like a Data Scientist" (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:
Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).
Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.
Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.
Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Science Fundamentals: Thinking Like a Data Scientist Process.
NEW QUESTION # 60
Which of the following is a DevOps Practice?
- A. All of the above
- B. Continuous build
- C. Continuous integration
- D. Continuous delivery
Answer: A
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 # 61
SpamAssassin has been developed to detect:
- A. None of the above
- B. Email with big attachments
- C. Email with virus
- D. Spam emails
Answer: D
Explanation:
Apache SpamAssassin is one of the most widely used open-source tools for spam email detection.
It applies a rule-based system combined with Bayesian filtering, heuristics, and collaborative filtering methods to classify incoming emails as spam or legitimate.
Option A (Spam emails): Correct, this is the main function.
Option B (Big attachments): Incorrect. Large attachment filtering is not its primary purpose.
Option C (Email with virus): Incorrect. That falls under antivirus or malware detection tools, not SpamAssassin.
Option D: Incorrect since A is valid.
Thus, the correct answer is Option A (Spam emails).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Email Filtering and Text Mining.
NEW QUESTION # 62
Which of these are open-source column-oriented databases?
- A. All of the above
- B. HBase
- C. Accumulo
- D. Both A and B
- E. Cassandra
Answer: A
Explanation:
Column-oriented databases store data by columns rather than by rows, enabling efficient queries over large datasets, especially in analytical workloads.
Cassandra (Option A): An open-source, highly scalable, distributed column-oriented NoSQL database.
HBase (Option B): An open-source, Hadoop-based, column-family NoSQL database modeled after Google BigTable.
Accumulo (Option C): An open-source, secure, sorted, distributed key/value store built on top of HDFS and based on Google BigTable.
Since all three (A, B, and C) are open-source column-oriented databases, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Columnar Databases & NoSQL Ecosystem.
NEW QUESTION # 63
Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized
- A. None of the above
- B. i, iii
- C. i, ii
- D. ii, iii
Answer: C
Explanation:
Images are broadly categorized based on how they store visual information:
Rasterized images (Option i):
Composed of a grid of pixels (bitmap).
Each pixel has color information.
Examples: JPEG, PNG, BMP.
Best for photos or complex visuals.
Vectorized images (Option ii):
Composed of paths defined by mathematical formulas.
Scalable without quality loss.
Examples: SVG, EPS, AI.
Best for logos, icons, and illustrations.
Sectorized images (Option iii):
Not a standard category in computer graphics.
Thus, image files are categorized into Rasterized and Vectorized, making Option A (i, ii) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Types & Multimedia Data Management.
NEW QUESTION # 64
......
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