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[General] Most-honored SDS Exam Brain Dumps: Senior Data Scientist display topping Study M

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【General】 Most-honored SDS Exam Brain Dumps: Senior Data Scientist display topping Study M

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DASCA Senior Data Scientist Sample Questions (Q62-Q67):NEW QUESTION # 62
A burn down chart shows:
  • A. The volume of work and features completed
  • B. The rate of reduction of budget for a project
  • C. The declining energy of the team
  • D. The number of hours worked after dark
Answer: A
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 # 63
Which of the following is TRUE about monetization exercise?
  • A. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
  • B. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
  • C. Both B and C
  • D. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
  • E. All of the above
Answer: E
Explanation:
The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.
Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.
Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.
Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).
Thus, all three statements are valid, making the correct answer Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Data Monetization Strategies.

NEW QUESTION # 64
Which of the following is an example of NLP?
  • A. Both A and C
  • B. Flagging e-mails as spam
  • C. Finding which text documents are about similar topics
  • D. Using Twitter to assess public sentiment
  • E. All of the above
Answer: E
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 # 65
What is TRUE for "rehashing"?
  • A. It requires a new hash function, which maps values into a larger range of integers
  • B. Both A and B
  • C. All of the above
  • D. Key/value pairs from the original table can be inserted into the new, larger one
  • E. Allocate a new, larger hash table in memory
Answer: C
Explanation:
Rehashing is a technique used in dynamic hash tables when the load factor (ratio of entries to bucket size) exceeds a certain threshold. It ensures efficient lookup, insertion, and deletion operations.
Option A (Correct): A larger hash table is allocated in memory to accommodate more entries.
Option B (Correct): A new hash function is typically required to map keys into the expanded table range.
Option C (Correct): All key-value pairs from the old table are re-inserted (rehashed) into the new table using the new hash function.
Since all three conditions (A, B, and C) are true, the best choice is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Data Structures & Hashing Techniques

NEW QUESTION # 66
SpamAssassin has been developed to detect:
  • A. Email with big attachments
  • B. Email with virus
  • C. None of the above
  • 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 # 67
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