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>> SDS Regualer Update <<

Quiz 2026 DASCA SDS Authoritative Regualer Update

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DASCA Senior Data Scientist Sample Questions (Q25-Q30):

NEW QUESTION # 25
Maximum Likelihood Estimation (MLE) is a way to frame:

A. Small class of problems in Data Science
B. Small class of problems in HDFS
C. Both A and C
D. Large class of problems in HDFS
E. Large class of problems in Data Science

Answer: E

Explanation:
Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a model by maximizing the likelihood function - i.e., finding the parameters that make the observed data most probable.
Option A: Correct. MLE provides a framework for a large class of problems in data science, including regression, classification, generative models, and probabilistic inference.
Option B: Incorrect - it applies to many problems, not just a small subset.
Option C & D: Incorrect. HDFS (Hadoop Distributed File System) is a storage technology, unrelated to MLE.
Option E: Incorrect because C is invalid.
Thus, the correct answer is Option A (Large class of problems in Data Science).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Foundations: Maximum Likelihood Estimation and Inference in Data Science.

NEW QUESTION # 26
Which of the following is True about Time Series Analysis?

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

Answer: E

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 # 27
What is TRUE for "rehashing"?

A. It requires a new hash function, which maps values into a larger range of integers
B. Key/value pairs from the original table can be inserted into the new, larger one
C. Allocate a new, larger hash table in memory
D. Both A and B
E. All of the above

Answer: E

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 # 28
Which of the following is CORRECT?
i. CDMO should NOT own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
ii. CDMO should own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
iii. CIO should NOT own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
iv. CIO should own the data lake, the data warehouse, or any of the underlying data architecture or technologies.

A. ii, iv
B. i, iii
C. i, iv
D. ii, iii

Answer: C

Explanation:
The Chief Data Monetization Officer (CDMO) is responsible for driving value extraction from data assets, not owning infrastructure. By contrast, the Chief Information Officer (CIO) traditionally owns technology infrastructure, including data lakes, warehouses, and data architectures.
Statement i: Correct. CDMO should NOT own data infrastructure. Their role is strategic - monetizing insights, not managing systems.
Statement ii: Incorrect. CDMO focuses on monetization, not architecture ownership.
Statement iii: Incorrect. CIO indeed does own these systems.
Statement iv: Correct. CIO owns the data lake, data warehouse, and underlying architectures/technologies.
Thus, the correct combination is i and iv # Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Roles in Data Leadership: CIO vs CDMO.

NEW QUESTION # 29
A burn down chart shows:

A. The declining energy of the team
B. The volume of work and features completed
C. The number of hours worked after dark
D. The rate of reduction of budget for a project

Answer: B

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 # 30
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