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DASCA Senior Data Scientist Sample Questions (Q59-Q64):

NEW QUESTION # 59
The Big Data Vision Workshop process is ideal for organizations who:

• A. Have a wealth of data that they do not know how to monetize
• B. Have a desire to leverage Big Data to transform their business but do not know where and how to start
• C. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
• D. All of the above
• E. Both A and B

Answer: D

Explanation:
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.

NEW QUESTION # 60
Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized

• A. i, ii
• B. None of the above
• C. ii, iii
• D. i, iii

Answer: A

Explanation:
Images are broadly categorized based on how they store visual information:
Rasterized images (Option i):
Composed of a grid of pixels (bitmap).
Each pixel has color information.
Examples: JPEG, PNG, BMP.
Best for photos or complex visuals.
Vectorized images (Option ii):
Composed of paths defined by mathematical formulas.
Scalable without quality loss.
Examples: SVG, EPS, AI.
Best for logos, icons, and illustrations.
Sectorized images (Option iii):
Not a standard category in computer graphics.
Thus, image files are categorized into Rasterized and Vectorized, making Option A (i, ii) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Types & Multimedia Data Management.

NEW QUESTION # 61
Which of the following is TRUE about Avro?

• A. Avro is a data serialization framework
• B. None of the above
• C. Avro is based on Remote Procedure Call (RPC)
• D. Both A and B

Answer: D

Explanation:
Apache Avro is a widely used framework within the Hadoop ecosystem for data serialization and data exchange.
Option A (Correct): Avro is a compact, fast, binary data serialization format. It allows efficient storage and exchange of structured data.
Option B (Correct): Avro supports Remote Procedure Call (RPC). It provides a framework for RPC communication, making it easier for distributed applications to exchange data across systems.
Option C: Correct, since both statements are true.
Option D: Incorrect because Avro is indeed both a serialization framework and RPC-based.
In data engineering workflows, Avro is valuable because it is schema-based (defined using JSON), highly interoperable, and ensures compatibility across different programming languages. This makes it essential in big data pipelines, Kafka messaging, and Hadoop ecosystem tools.
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem Tools & Data Serialization Techniques.

NEW QUESTION # 62
What is DevOps?

• A. Software Operations
• B. Quality Assurance
• C. Software Development
• D. All

Answer: D

Explanation:
DevOps is not just about coding (development) or system administration (operations). It is a holistic cultural and technical practice that unifies:
Software Development (Option A): Writing and building applications.
Software Operations (Option B): Deploying, monitoring, and maintaining systems in production.
Quality Assurance (Option C): Ensuring the reliability, security, and performance of applications through testing and automation.
Thus, DevOps encompasses all three dimensions, making the correct answer Option D (All).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: DevOps & Continuous Delivery.

NEW QUESTION # 63
Maximum Likelihood Estimation (MLE) is a way to frame:

• A. Both A and C
• B. Large class of problems in HDFS
• C. Small class of problems in HDFS
• D. Small class of problems in Data Science
• 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 # 64
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