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Exam DASCA SDS Discount - SDS Exam Cram

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DASCA Senior Data Scientist Sample Questions (Q73-Q78):

NEW QUESTION # 73
Which of the following is correct?

A. Both A and B
B. All of the above
C. A data frame is a table with rows and columns
D. The central object in Pandas is called a DataFrame
E. DataFrame is similar to SQL tables or R data frames

Answer: B

Explanation:
Pandas is one of the most widely used Python libraries for data analysis and manipulation. Its central object is the DataFrame.
Option A: Correct. DataFrame is the core data structure in Pandas.
Option B: Correct. DataFrame resembles SQL tables and R data frames, supporting row/column indexing, joins, and grouping.
Option C: Correct. A DataFrame is essentially a 2D labeled table consisting of rows and columns.
Option D: Correct, but not fully inclusive.
Option E: Correct, since all of A, B, and C are true.
Thus, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Pandas Data Structures.

NEW QUESTION # 74
Which of the following is TRUE for data lake?

A. The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive
B. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized
C. The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
D. The data lake can make both of the Business Intelligence and Data Science environments more agile and more productive
E. None of the above

Answer: B,C,D

Explanation:
But per MCQ single-choice format # answer: A (though ideally A, B, C are correct).
A data lake is a centralized repository designed to store raw, structured, semi-structured, and unstructured data at scale. It provides:
Agility and productivity (Option A): Data lakes support flexible ingestion and faster access, making BI and data science environments more efficient.
Data integration (Option B): They handle multiple types of data, enabling advanced analytics and machine learning use cases.
Data as an asset (Option C): They shift perspective, treating data as a strategic resource, not just a storage cost.
Option D: Incorrect. Data lakes improve agility, not reduce it.
Option E: Incorrect, since multiple true statements exist.
Thus, the correct choice per DASCA context is Option A (with B and C also being true).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering: Data Lakes vs Warehouses.

NEW QUESTION # 75
Which of the following is a trend analysis component of time series decomposition?

A. Irregular
B. Seasonal
C. Both A and B
D. All of the above
E. Cyclical

Answer: D

Explanation:
Time series decomposition breaks down data into components to better understand underlying patterns and support forecasting. The main components are:
Trend: Long-term progression (upward or downward).
Seasonal: Repeating short-term patterns (e.g., monthly or quarterly).
Cyclical (Option A): Medium- to long-term cycles (e.g., business cycles).
Irregular/Residual (Option C): Random, unpredictable variations.
Since trend analysis involves examining cyclical, seasonal, and irregular components, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Time Series Decomposition and Trend Analysis.

NEW QUESTION # 76
Which of the following is a DevOps Practice?

A. Continuous build
B. Continuous delivery
C. Continuous integration
D. All of the above

Answer: D

Explanation:
DevOps is a collaborative practice that integrates software development (Dev) and IT operations (Ops) to shorten development cycles and deliver applications reliably. Common DevOps practices include:
Continuous Build (Option A): Automating compilation and packaging of source code to ensure consistent builds.
Continuous Integration (Option B): Developers frequently merge code into a shared repository, which is automatically tested to catch integration issues early.
Continuous Delivery (Option C): Automating software release pipelines so applications can be deployed to production quickly and reliably.
Since all of these are essential DevOps practices, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: DevOps Practices in Data Science Projects.

NEW QUESTION # 77
Spark is written in:

A. Python
B. C
C. Java
D. Scala
E. C++

Answer: D

Explanation:
Apache Spark is an open-source distributed computing framework widely used for big data processing and machine learning pipelines.
The core implementation of Spark is written in Scala (Option A), which runs on the JVM (Java Virtual Machine).
Spark also provides APIs for Java, Python (PySpark), R, and SQL, but its native language is Scala.
Options C (C) and D (C++) are incorrect; Spark is not written in these languages.
Python (Option E) is a supported API, but Spark itself is not written in Python.
Thus, the correct answer is Scala (Option A).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools for Big Data & Distributed Computing.

NEW QUESTION # 78
......

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