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DASCA Senior Data Scientist Sample Questions (Q63-Q68):

NEW QUESTION # 63
ARIMA model is:

A. Autoresponsive moving average
B. Autoreactive moving average
C. All of the above
D. Autoregressive moving average
E. Autointeractive moving average

Answer: D

Explanation:
ARIMA stands for AutoRegressive Integrated Moving Average, one of the most widely used models for time series forecasting.
AutoRegressive (AR): Model uses past values of the variable to predict future values.
Integrated (I): Differencing is applied to make the time series stationary.
Moving Average (MA): Model incorporates past forecast errors into predictions.
Option B: Correct - autoregressive + moving average is part of ARIMA's name.
Options A, C, D: Incorrect because these terms are not recognized statistical modeling frameworks.
Option E: Incorrect, since only B is valid.
Thus, the correct answer is Option B.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Time Series Models (AR, MA, ARIMA).

NEW QUESTION # 64
HDFS supports which quotas?

A. Space quotas
B. Both A and B
C. None of the above
D. Name quotas

Answer: B

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 # 65
Self-driving car is an example of:

A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. All of the above

Answer: C

Explanation:
Self-driving cars (autonomous vehicles) are an application of Reinforcement Learning (RL) in machine learning:
In RL, an agent (car) interacts with an environment (roads, obstacles, traffic) and learns to maximize rewards (e.g., safe driving, efficient navigation).
The system improves performance through trial-and-error learning, guided by reward signals such as staying in a lane or avoiding collisions.
Supervised learning (A): Used in some supporting tasks like image recognition (e.g., identifying stop signs), but not the core paradigm for self-driving.
Unsupervised learning (B): Useful for clustering sensor data, but again not the main paradigm.
Reinforcement learning (C): Correct, since self-driving fundamentally depends on RL decision-making.
Thus, the correct answer is Option C (Reinforcement Learning).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Paradigms: Reinforcement Learning and Autonomous Systems.

NEW QUESTION # 66
Bernoulli random variable is a type of:

A. Both A and B
B. Discrete random variable
C. Sometimes Discrete or sometimes Continuous random variable
D. Continuous random variable

Answer: B

Explanation:
A Bernoulli random variable is the simplest form of discrete random variable.
It can take only two values:
1 with probability p (success).
0 with probability (1 - p) (failure).
Since the outcomes are finite (binary), it is a discrete random variable.
Option B (Continuous): Incorrect, as continuous variables take values from an interval (e.g., real numbers).
Option C and D: Incorrect, as Bernoulli is always discrete, never continuous.
Thus, the correct answer is Option A (Discrete random variable).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Probability & Statistics for Data Science: Random Variables and Bernoulli Distribution.

NEW QUESTION # 67
Which of the following is TRUE about monetization exercise?

A. All of the above
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. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
D. Both B and C
E. The monetization exercise provides an opportunity to uncover new product and/or service opportunities

Answer: A

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 # 68
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