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DASCA Senior Data Scientist Sample Questions (Q42-Q47):

NEW QUESTION # 42
ARIMA model is:

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

Answer: C

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 # 43
Which of the following is True about Time Series Analysis?

A. Predicting when/whether an event will occur, such as a failure of the machine generating the data
B. Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through
C. All of the above
D. Both A and B
E. Projecting the value of the time series at future points in time, such as a stock whose price we want to predict

Answer: C

Explanation:
Time Series Analysis (TSA) is the process of analyzing data collected sequentially over time to extract meaningful insights.Applications include:
Option A: Correct. Event prediction (e.g., failure detection in IoT or predictive maintenance).
Option B: Correct. Forecasting future values (e.g., stock price, sales forecasting).
Option C: Correct. Pattern discovery in large-scale time series datasets using clustering, anomaly detection, or seasonality detection.
Since all three are true, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Time Series Analysis and Forecasting.

NEW QUESTION # 44
Example of amortized performance is:

A. All of the above
B. Python dictionaries
C. Hadoop dictionaries
D. MapReduce dictionaries
E. HDFS dictionaries

Answer: B

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 # 45
Bernoulli random variable is a type of:

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

Answer: A

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 # 46
What is Scrumban?

A. It is Kanban
B. It combines the principles of Scrum and Kanban into a push-based system
C. It combines the principles of Scrum and Kanban into a pull-based system
D. It is Scrum

Answer: C

Explanation:
Scrumban is a hybrid Agile methodology that merges Scrum and Kanban to take advantage of the strengths of both.
From Scrum, Scrumban adopts structured sprint planning, roles, and iterative review cycles.
From Kanban, it borrows the visual board system, continuous workflow management, and the pull-based approach, where tasks are pulled into the workflow only when capacity is available.
The pull-based system ensures that teams do not overload themselves and helps manage work-in-progress (WIP) effectively. This makes Scrumban particularly suitable for projects with frequent changes, ongoing maintenance tasks, or teams transitioning from Scrum to Kanban.
Thus, the correct answer is Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Project Management Techniques for Data Science.

NEW QUESTION # 47
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