New 2025 SDS Dumps for DASCA Data Scientist Certified Exam Questions and Answer
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NEW QUESTION # 10
Which of the following is NOT Structured data?
- A. None of the above
- B. XML
- C. Data tables
- D. CSV files
- E. Relational databases
Answer: B
Explanation:
Structured data refers to highly organized data stored in predefined formats such as rows and columns.
Relational databases (Option A): Structured.
Data tables (Option B): Structured.
CSV files (Option C): Structured because they are tabular with rows and columns.
XML (Option D): Not structured but semi-structured because it stores hierarchical information with tags and attributes.
Thus, the correct answer is Option D (XML).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Structured vs Semi- structured Data.
NEW QUESTION # 11
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. i, iv
- B. ii, iii
- C. ii, iv
- D. i, iii
Answer: A
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 # 12
Semi-structured data does NOT include:
- A. Schema-full data
- B. File systems
- C. Database system
- D. Scientific data
Answer: A
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 # 13
Self-driving car is an example of:
- A. Unsupervised learning
- B. Reinforcement learning
- C. Supervised learning
- D. All of the above
Answer: B
Explanation:
Self-driving cars (autonomous vehicles) are an application of Reinforcement Learning (RL) in machine learning:
In RL, an agent (car) interacts with an environment (roads, obstacles, traffic) and learns to maximize rewards (e.g., safe driving, efficient navigation).
The system improves performance through trial-and-error learning, guided by reward signals such as staying in a lane or avoiding collisions.
Supervised learning (A): Used in some supporting tasks like image recognition (e.g., identifying stop signs), but not the core paradigm for self-driving.
Unsupervised learning (B): Useful for clustering sensor data, but again not the main paradigm.
Reinforcement learning (C): Correct, since self-driving fundamentally depends on RL decision-making.
Thus, the correct answer is Option C (Reinforcement Learning).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Paradigms: Reinforcement Learning and Autonomous Systems.
NEW QUESTION # 14
Which of the following is TRUE for Business Metamorphosis?
- A. All of the above
- B. Both A and C
- C. Business Metamorphosis exercise can uncover Big Data requirements around decisions, analytics and data sources that can be leveraged to transform or metamorphose your organization's business model
- D. The Business Metamorphosis phase helps drive an organization's core business model through the analytic insights gathered as the organization traverses the Big Data Business Model Maturity Index
- E. The Business Metamorphosis phase is where organizations integrate the insights that they captured about their customers' usage patterns, product performance behaviors, and overall market trends to transform their business models
Answer: A
Explanation:
Business Metamorphosis is the most advanced phase in the Big Data Business Model Maturity Index (BDBMMI), where organizations fundamentally transform their business models through analytics-driven insights.
Option A: Correct. This phase helps organizations identify big data requirements related to decisions, analytics, and sources that drive business transformation.
Option B: Correct. Organizations integrate customer usage patterns, product behaviors, and market trends into their decision-making to redesign or innovate their business model.
Option C: Correct. Business Metamorphosis ensures that the core business model evolves continuously, guided by insights derived across maturity stages.
Since all are correct, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Business Model Maturity Index.
NEW QUESTION # 15
Which of the following is TRUE about monetization exercise?
- A. All of the above
- B. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
- C. Both B and C
- D. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
- E. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
Answer: A
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 # 16
A burn down chart shows:
- A. The number of hours worked after dark
- B. The rate of reduction of budget for a project
- C. The volume of work and features completed
- D. The declining energy of the team
Answer: C
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 # 17
The main purpose of a Statement Of Work (SOW) is to get:
- A. What expectations are realistic
- B. None of the above
- C. All of the above
- D. Everybody on the same page about what work should be done
- E. What the priorities are
Answer: C
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 # 18
Which of these are open-source column-oriented databases?
- A. Accumulo
- B. Cassandra
- C. Both A and B
- D. All of the above
- E. HBase
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 # 19
Which of the following is correct about customer lifetime value (CLTV)?
i. Most organizations determine the current customer lifetime value (CLTV) based on historic sales over past
12 to 18 months
ii. The goal of the CLTV score is to help marketing and store personnel to determine the "value" of a customer
- A. Only ii
- B. Only i
- C. Both i and ii
Answer: C
Explanation:
Customer Lifetime Value (CLTV) is a predictive metric estimating the total revenue a business can reasonably expect from a customer during their entire relationship.
Statement i: Correct. Many organizations calculate CLTV using historic transactional data, often looking at sales records over the past 12-18 months to establish baselines.
Statement ii: Correct. The primary purpose of CLTV is to help marketing, sales, and retail teams understand customer value, enabling them to allocate budgets effectively for retention, promotions, and personalized marketing.
Thus, both statements are correct # Option C (Both i and ii).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: CLTV Metrics and Marketing Analytics.
NEW QUESTION # 20
Which of the following architectural techniques is used for parallel processing?
- A. Both A and B
- B. Both B and C
- C. The SuperVector Technique
- D. The Superscalar Technique
- E. Very Long Instruction Words (VLIW) Technique
Answer: A
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 # 21
Which of the following is the common evolutionary algorithm of neural networks?
- A. All of the above
- B. Genetic algorithm
- C. Evolution strategy
- D. Genetic programming
Answer: A
Explanation:
Evolutionary algorithms are optimization techniques inspired by natural selection. They are often applied in neural network optimization for evolving architectures, weights, or hyperparameters.
Genetic Algorithm (Option A): Uses selection, crossover, and mutation to evolve neural network parameters.
Genetic Programming (Option B): Evolves computer programs or expressions; can be applied to evolve neural network structures.
Evolution Strategy (Option C): Focuses on optimization using mutation and adaptation of parameters.
Since all are used as evolutionary approaches in neural networks, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Neural Networks: Evolutionary Algorithms in Machine Learning.
NEW QUESTION # 22
Semi-structured data does NOT include:
- A. Schema-full data
- B. File systems
- C. Database system
- D. Scientific data
Answer: A
NEW QUESTION # 23
Example of amortized performance is:
- A. Hadoop dictionaries
- B. Python dictionaries
- C. MapReduce dictionaries
- D. HDFS dictionaries
- E. All of the above
Answer: B
Explanation:
Amortized performance refers to averaging the cost of operations over a sequence of actions, ensuring that while some operations may be costly, the overall average time per operation remains efficient.
Python Dictionaries (Option B): Implemented using hash tables. Insertions, deletions, and lookups typically run in O(1) average time, but occasionally require rehashing (costly). The high cost of rehashing is spread over many operations, giving amortized constant-time performance.
Option A (Hadoop dictionaries): Not standard terminology.
Option C (HDFS dictionaries): HDFS doesn't use dictionary structures in this sense.
Option D (MapReduce dictionaries): MapReduce uses key-value pairs, but amortized dictionary performance is not its focus.
Thus, the correct answer is Option B (Python dictionaries).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Hash Tables & Amortized Analysis.
NEW QUESTION # 24
Which of the following is TRUE for Chief Data Monetization Officer (CDMO)?
i. CDMO should focus on driving and deriving value from the organization's data and analytic assets.
ii. The CDMO should own the organization's investment decisions with respect to data and analytics.
iii. CDMO should have revenue and margin responsibilities.
- A. i, ii
- B. All of the above
- C. ii, iii
Answer: B
Explanation:
The Chief Data Monetization Officer (CDMO) is a C-level executive role emerging in data-driven organizations. Their focus is on turning data into business value and revenue growth.
Statement i: Correct. The CDMO is responsible for value realization from data and analytics investments.
Statement ii: Correct. The CDMO owns decisions around investment prioritization for data initiatives.
Statement iii: Correct. Since monetization is directly linked to business performance, the CDMO must be accountable for revenue and margin impact.
Hence, all three statements are correct, making the right answer Option C (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Leadership & Monetization Roles.
NEW QUESTION # 25
Exploratory analytic algorithms help the Data Science team to better:
- A. Gain a high-level understanding of relationships
- B. Both A and B
- C. All of the above
- D. Understand the data content
- E. Understand patterns in the data
Answer: C
Explanation:
Exploratory analytics (often referred to as Exploratory Data Analysis - EDA) is a fundamental step in data science, enabling practitioners to discover initial insights, detect anomalies, and understand the structure of datasets before applying predictive or prescriptive modeling.
Option A (Understand the data content): Correct. EDA techniques (descriptive statistics, summary tables, profiling) reveal missing values, data types, and distributions.
Option B (Gain a high-level understanding of relationships): Correct. Correlation analysis, scatter plots, and cross-tabulations help identify dependencies between variables.
Option C (Understand patterns in the data): Correct. Visualization and clustering methods help discover hidden structures, seasonalities, and outliers.
Since exploratory algorithms contribute to all of these objectives, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Exploratory Analytics & EDA.
NEW QUESTION # 26
Which of the following is an example of graphical model?
- A. Bayesian Networks
- B. Both A and B
- C. Both A and C
- D. Markov Random Fields
- E. Geographical Networks
Answer: B
Explanation:
Graphical models are probabilistic models that represent variables and dependencies using graphs:
Markov Random Fields (Option A): Undirected graphical models that capture joint distributions over variables with neighborhood dependencies.
Bayesian Networks (Option B): Directed acyclic graphical models that encode conditional dependencies between random variables.
Geographical Networks (Option C): While they are graphs, they are not probabilistic graphical models used in statistics/ML.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graphical Models (Bayesian Networks
& Markov Random Fields).
NEW QUESTION # 27
The aim of developing Scribe was to offer reliability with using:
- A. Extensive protocols
- B. None of the above
- C. High disk space usage
- D. Complex protocols
Answer: B
Explanation:
Scribe is a server developed by Facebook for aggregating log data streamed in real time from many servers.
Its primary aim was reliability and simplicity in collecting logs at scale.
Unlike other systems, Scribe was designed to avoid complex or extensive protocols and to ensure lightweight log transfer.
It also avoids unnecessary high disk usage, relying instead on efficient pipelines.
Thus, the correct answer is Option D (None of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Tools: Log Collection Systems (Scribe, Kafka, Flume).
NEW QUESTION # 28
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