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Valid SDS Exam Bootcamp - SDS Top Questions

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DASCA Senior Data Scientist Sample Questions (Q72-Q77):

NEW QUESTION # 72
Which of the following errors refers to the wrong negation of a true null hypothesis?

Answer: A

Explanation:
In hypothesis testing, two main types of errors are defined:
Type I Error (Option A): Occurs when the null hypothesis (H#) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.
Type II Error (Option B): Occurs when the null hypothesis (H#) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.
Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.
Thus, the "wrong negation of a true null hypothesis" refers to a Type I Error (false positive).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Foundations in Data Science: Hypothesis Testing & Errors.


NEW QUESTION # 73
ARIMA model is:

Answer: E

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 # 74
Which of the following is the most important part of Hadoop?

Answer: C

Explanation:
The Hadoop ecosystem consists of multiple components, but the two core components that define Hadoop are:
HDFS (Hadoop Distributed File System): Provides fault-tolerant, scalable storage across distributed clusters.
It is the backbone for storing massive datasets in a distributed fashion.
MapReduce Framework: Provides the parallel computing and data processing layer in Hadoop, enabling batch analysis over distributed datasets.
Option A: Correct, HDFS is essential.
Option B: Correct, MapReduce is essential.
Option C: Incorrect, Spark is a newer processing framework, but it is not originally part of Hadoop core.
Option D: Correct answer since both HDFS and MapReduce are considered the fundamental parts of Hadoop.
Option E: Incorrect, because Spark is not a core Hadoop component (though it integrates with Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystems: Hadoop Architecture & Components.


NEW QUESTION # 75
Machine learning can be used in:

Answer: A

Explanation:
Machine Learning has broad applications across industries and technologies:
Fraud Detection (Option A): Detecting anomalies in financial transactions, credit card usage, and cybersecurity threats.
Web Search Results (Option B): Ranking algorithms (e.g., Google's PageRank enhanced by ML techniques) improve relevance of search queries.
Real-time Ads (Option C): Online ad systems use reinforcement learning and recommendation models to target ads dynamically.
Pattern & Image Recognition (Option D): ML (especially deep learning) powers facial recognition, handwriting recognition, medical imaging, etc.
Since ML is used in all these applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning Across Domains.


NEW QUESTION # 76
Semi-structured data does NOT include:

Answer: B

Explanation:
Semi-structured data falls between structured data (e.g., relational databases with fixed schema) and unstructured data (e.g., free text, audio, video). It typically includes irregular or flexible schema information, such as XML, JSON, email data, or log files.
Option A (Database systems): Correct, databases may hold semi-structured content (e.g., JSON or XML columns).
Option B (File systems): Correct, file-based storage (logs, JSON, Avro, CSV) often contains semi-structured data.
Option C (Scientific data): Correct, many scientific applications generate semi-structured data formats (sensor readings, genomic sequences, etc.).
Option D (Schema-full data): Correct Answer. Schema-full (strict schema-defined relational tables) represent structured data, not semi-structured.
Thus, semi-structured data does NOT include schema-full data.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Data Types & Sources.


NEW QUESTION # 77
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