[May 02, 2026] Free DASCA SDS Exam Questions and Answer
Verified SDS dumps Q&As Latest SDS Download
NEW QUESTION # 22
OCR (Optical Character Recognition) is an application used for:
- A. Machine learning
- B. MapReduce
- C. Data mining
- D. Big Data Analytics
Answer: A
Explanation:
Optical Character Recognition (OCR) is the process of automatically recognizing and converting different types of documents - such as scanned paper documents, PDFs, or images - into editable and searchable text.
OCR systems use Machine Learning (ML) and Computer Vision techniques to detect and classify patterns of characters in images.
Algorithms like Convolutional Neural Networks (CNNs) are commonly used for image-based OCR.
While OCR may indirectly contribute to data mining or big data workflows, the core application is based on machine learning, where models are trained to classify and recognize text patterns.
Thus, OCR is primarily a Machine Learning application, making Option B correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: OCR and Pattern Recognition.
NEW QUESTION # 23
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
- A. Trend analysis
- B. High Curve Equivalent (HCE)
- C. Normal Curve Equivalent (NCE)
- D. Medium Curve Equivalent (MCE)
- E. None of the above
Answer: C
Explanation:
Normal Curve Equivalent (NCE) scores are standardized scores designed to:
Range between 1 and 99.
Be comparable to percentile ranks but with the advantage of equal-interval properties like Z-scores.
This makes NCE scores useful in educational assessments, survey analysis, and statistical modeling.
Option A (Trend analysis): Incorrect. Not related to score standardization.
Option B (Correct): NCE fits the definition perfectly.
Option C (HCE) & D (MCE): Not recognized standard measures in statistics.
Option E: Incorrect, since Option B is valid.
Thus, the correct answer is Option B: Normal Curve Equivalent (NCE).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Methods in Data Science: Z-scores, Percentiles, and NCE.
NEW QUESTION # 24
IoT is built on:
- A. Networks of data gathering devices
- B. Cloud Computing
- C. Both A and B
- D. None of the above
Answer: C
Explanation:
The Internet of Things (IoT) is an ecosystem of interconnected devices that collect, transmit, and analyze data. IoT relies on two critical foundations:
Option A (Cloud Computing): IoT generates massive amounts of data, and cloud platforms provide scalable storage, analytics, and computing resources for real-time and batch processing.
Option B (Networks of data gathering devices): IoT relies on physical devices - sensors, smart appliances, industrial machines - that collect and transmit data through networks (Wi-Fi, Bluetooth, 5G, LPWAN).
Thus, IoT is fundamentally built on both cloud computing and networks of devices, making Option C correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data & IoT Ecosystem Fundamentals.
NEW QUESTION # 25
The grid computing environment uses a middleware to:
- A. Combine computing resources
- B. Divide computing resources
- C. Both A and B
- D. None of the above
Answer: C
Explanation:
Grid computing is a distributed computing model where resources (CPU, memory, storage) are pooled across multiple systems to solve large-scale problems.
Option A (Divide): Middleware helps allocate or divide resources dynamically to different tasks.
Option B (Combine): Middleware integrates diverse resources into a unified system, making them accessible for parallel computing.
Option C: Correct - middleware is the "glue" that enables both combining and dividing resources seamlessly across distributed nodes.
Option D: Incorrect.
Thus, grid computing middleware both combines and divides resources, making Option C correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Grid and Distributed Computing.
NEW QUESTION # 26
Which of the following statements is correct?
- A. Apache claimed that Spark is able to run parallel jobs 1000 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
- B. Apache claimed that Spark is able to run parallel jobs 10 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
- C. Apache claimed that Spark is able to run parallel jobs 50 times faster in memory and 5 times faster on disk in comparison to the traditional Hadoop MapReduce
- D. Apache claimed that Spark is able to run parallel jobs 100 times faster in memory and 10 times faster on disk in comparison to the traditional Hadoop MapReduce
Answer: D
Explanation:
Apache Spark is a distributed computing framework designed as an improvement over Hadoop's MapReduce.
According to the official Apache Spark documentation:
Spark can run workloads up to 100x faster in memory.
Spark can run workloads up to 10x faster on disk.
This performance gain comes from Spark's use of in-memory computation, DAG execution engine, and optimized query execution, compared to the slower, disk-heavy Hadoop MapReduce framework.
Thus, the correct statement is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Spark vs Hadoop Performance Comparisons.
NEW QUESTION # 27
Which of the following is TRUE about monetization exercise?
- A. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
- B. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
- C. Both B and C
- D. All of the above
- E. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
Answer: D
Explanation:
The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.
Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.
Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.
Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).
Thus, all three statements are valid, making the correct answer Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Data Monetization Strategies.
NEW QUESTION # 28
Which of these are open-source column-oriented databases?
- A. Cassandra
- B. HBase
- C. Accumulo
- D. All of the above
- E. Both A and B
Answer: D
Explanation:
Column-oriented databases store data by columns rather than by rows, enabling efficient queries over large datasets, especially in analytical workloads.
Cassandra (Option A): An open-source, highly scalable, distributed column-oriented NoSQL database.
HBase (Option B): An open-source, Hadoop-based, column-family NoSQL database modeled after Google BigTable.
Accumulo (Option C): An open-source, secure, sorted, distributed key/value store built on top of HDFS and based on Google BigTable.
Since all three (A, B, and C) are open-source column-oriented databases, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Columnar Databases & NoSQL Ecosystem.
NEW QUESTION # 29
Which of the following is correct for Markov chain?
- A. A Markov chain is a sequence of random variables X1, X2
- B. A Markov chain is a sequence of fixed variables X1, X2
- C. Both B and C
- D. A Markov chain is the state of a system at sequential points in time
- E. Both A and B
Answer: C
Explanation:
A Markov chain is a stochastic process describing a sequence of possible events, where the probability of each event depends only on the state attained in the previous step (the Markov property).
Option A: Incorrect. The variables are random, not fixed.
Option B: Correct. Markov chains represent the state of a system at sequential time points.
Option C: Correct. A Markov chain is indeed a sequence of random variables {X1, X2, ...} that satisfy the Markov property.
Option D: Incorrect, since A is wrong.
Option E: Correct, because both B and C are valid.
Thus, the correct answer is Option E (Both B and C).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Probabilistic Models: Markov Chains.
NEW QUESTION # 30
What is TRUE for "rehashing"?
- A. Allocate a new, larger hash table in memory
- B. It requires a new hash function, which maps values into a larger range of integers
- C. Key/value pairs from the original table can be inserted into the new, larger one
- D. All of the above
- E. Both A and B
Answer: D
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 # 31
Spark is written in:
- A. C
- B. Python
- C. Scala
- D. C++
- E. Java
Answer: C
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 # 32
Which of the following is NOT used to implement Agile?
- A. Extreme Programming (XP)
- B. Six Sigma
- C. Kanban
- D. Scrum
Answer: B
Explanation:
Agile is a project management and product development methodology that emphasizes flexibility, iterative development, collaboration, and adaptability. Common frameworks that implement Agile principles include:
Scrum (Option A): A widely used Agile framework where work is divided into time-boxed sprints, roles are clearly defined (Scrum Master, Product Owner, Development Team), and progress is tracked via tools like burn down charts.
Kanban (Option B): A visual Agile method that manages workflow using a pull-based system with Kanban boards to visualize tasks and control work in progress (WIP).
Extreme Programming (XP, Option D): Another Agile method that focuses on high-quality software through technical practices like continuous integration, test-driven development (TDD), and pair programming.
By contrast:
Six Sigma (Option C): Six Sigma is a process improvement methodology that emphasizes reducing defects and improving quality through statistical analysis. It is not considered an Agile implementation framework.
Thus, the correct answer is Six Sigma (C).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Methodologies and Project Management in Data Science.
NEW QUESTION # 33
Which of the following architectural techniques is used for parallel processing?
- A. The Superscalar Technique
- B. Both A and B
- C. Both B and C
- D. The SuperVector Technique
- E. Very Long Instruction Words (VLIW) Technique
Answer: B
Explanation:
Parallel processing architectures are designed to execute multiple instructions or operations simultaneously:
Superscalar Technique (Option A): Uses multiple execution units so that several instructions can be issued and executed in parallel within a single CPU cycle.
VLIW Technique (Option B): Uses very long instruction words, where multiple operations are encoded into a single instruction and executed in parallel.
SuperVector (Option C): Refers to vector processors, which process large arrays of data but is not classified as a mainstream architectural parallel technique in modern CPU design.
Therefore, the primary architectural techniques for parallel processing are Superscalar and VLIW, making Option D (Both A and B) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Parallel and Distributed Processing.
NEW QUESTION # 34
ElementTree sub-library gives us direct access to:
- A. Parse tree of the XML
- B. Insert tree of the XML
- C. Copy tree of the XML
- D. None of the above
- E. Delete tree of the XML
Answer: A
Explanation:
In Python, the ElementTree module (part of the standard library xml.etree.ElementTree) provides a simple and efficient API for parsing and creating XML data.
The main feature of ElementTree is its ability to provide direct access to the parse tree of an XML document.
This allows developers to:
Parse XML into an in-memory tree structure.
Traverse, search, modify, and extract information from XML elements.
Write back changes into XML files.
Options B, C, and D (Delete, Copy, Insert tree) are not standard terminology in XML handling with ElementTree. While you can delete, insert, or copy elements, the module itself primarily gives parse tree access.
Thus, the correct answer is Option A (Parse tree of the XML).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: XML/JSON Handling in Python.
NEW QUESTION # 35
Which of the following is NOT a correct situation to use Agile?
- A. When the final product isn't clearly defined
- B. None of the above
- C. When changes need to be implemented during the entire process
- D. When clients/stakeholders need to be able to change the scope
Answer: B
Explanation:
Agile methodology is widely adopted in data science projects because these projects often involve uncertain goals, exploratory analysis, and changing requirements. Agile thrives in environments where iteration, collaboration, and adaptability are necessary.
Option A: True for Agile. If the final product is unclear (common in data science), Agile works well because it allows incremental discovery and iterative prototyping.
Option B: True for Agile. Agile frameworks (Scrum, Kanban) emphasize flexibility, which means the scope can evolve as stakeholders learn more from data and models.
Option C: True for Agile. Agile welcomes continuous changes through iterative sprints and feedback loops.
This adaptability is crucial in machine learning model development where data insights often reshape project direction.
Since all three situations are valid for Agile, the correct answer to "Which is NOT correct?" is None of the above (Option D).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science & Agile Methodologies in Data Projects.
NEW QUESTION # 36
The main purpose of a Statement Of Work (SOW) is to get:
- A. Everybody on the same page about what work should be done
- B. What expectations are realistic
- C. What the priorities are
- D. All of the above
- E. None of the above
Answer: D
Explanation:
A Statement of Work (SOW) is a formal document that defines the scope, objectives, deliverables, timeline, and expectations of a project. In data science and IT projects, it ensures:
Clarity of scope (Option A): Everyone understands exactly what work should be done.
Clear priorities (Option B): It defines what is most critical for success.
Realistic expectations (Option C): It aligns stakeholders by setting measurable and achievable goals.
Since all of these are essential purposes of an SOW, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications: Project Governance and SOW.
NEW QUESTION # 37
......
Use Real Dumps - 100% Free SDS Exam Dumps: https://pass4sure.testvalid.com/SDS-valid-exam-test.html