SDS Certified Questions | SDS Best Preparation Materials

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DASCA Senior Data Scientist Sample Questions (Q84-Q89):

NEW QUESTION # 84
Which of the following is TRUE about monetization exercise?

Answer: B

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 # 85
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 # 86
SpamAssassin has been developed to detect:

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 # 87
Which of these statements reflects a null hypothesis?

Answer: E

Explanation:
A null hypothesis (H#) is the default assumption in statistical testing that there is no effect, no difference, or no relationship between variables.
Option A: Correct. This states explicitly that there is no relationship between caffeine consumption and performance # fits the definition of a null hypothesis.
Options B, C, D, E: These all hypothesize differences or relationships # they are examples of alternative hypotheses (H#), not null.
Thus, the correct answer is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistics in Data Science: Hypotheses, Errors, and Testing.


NEW QUESTION # 88
HDFS supports which quotas?

Answer: A

Explanation:
HDFS (Hadoop Distributed File System) provides quota management to control and monitor resource usage across directories:
Name Quotas (Option A): Limits the number of files and directories that can be created in a given HDFS directory. Helps prevent excessive metadata growth.
Space Quotas (Option B): Limits the total disk space consumed by files within a directory. Helps in capacity planning and avoiding storage overuse.
Since HDFS supports both types, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: HDFS Management and Quotas.


NEW QUESTION # 89
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