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NEW QUESTION # 28
Which of the following is NOT a correct situation to use Agile?
- A. When the final product isn't clearly defined
- B. When clients/stakeholders need to be able to change the scope
- C. None of the above
- D. When changes need to be implemented during the entire process
Answer: C
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 # 29
Which of the following architectural techniques is used for parallel processing?
- A. The SuperVector Technique
- B. Both B and C
- C. Both A and B
- D. The Superscalar Technique
- E. Very Long Instruction Words (VLIW) Technique
Answer: C
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 # 30
Which of the following is NOT a process of Use Case?
- A. Brainstorm the outcomes that the key stakeholders need to answer to facilitate making the decisions
- B. Identify your key business stakeholders
- C. Capture the decisions that the key business stakeholders need to make in order to support the organization's key business initiatives
- D. Brainstorm the questions that the key stakeholders need to answer to facilitate making the decisions
- E. Understand your organization's key business initiatives or business challenge
Answer: A
Explanation:
Use Case Development in data science projects involves identifying business needs and mapping analytics to business decisions. The standard steps include:
Option A: Understanding the key initiatives or challenges.
Option B: Identifying the key stakeholders.
Option C: Capturing the decisions stakeholders must make.
Option E: Brainstorming the questions stakeholders need answered to support decisions.
However:
Option D (Brainstorm the outcomes stakeholders need to answer): Incorrect phrasing. It is not "outcomes" that are brainstormed but questions and decisions.
Thus, the correct answer is Option D.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Use Case Development Process.
NEW QUESTION # 31
Business Intelligence (BI) is:
- A. BI focuses on "What happened?"
- B. BI focuses on descriptive analytics
- C. Both B and C
- D. Both A and B
- E. BI focuses on reporting on the future state of the business
Answer: D
Explanation:
Business Intelligence (BI) is primarily focused on descriptive analytics and reporting - understanding historical and current business performance.
Option A (Descriptive analytics): Correct. BI uses dashboards, reports, and OLAP tools to summarize what has occurred in the past.
Option B ("What happened?"): Correct. BI answers retrospective questions by analyzing transactional and operational data.
Option C (Future state): Incorrect. Predicting future business outcomes falls under predictive analytics or advanced analytics, not BI.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization & BI: Descriptive Analytics and Reporting.
NEW QUESTION # 32
Which of the following is NOT a part of Internal Process Optimization?
- A. Business Insights
- B. Business Metamorphosis
- C. Business Optimization
- D. None of the above
- E. Business Monitoring
Answer: B
Explanation:
Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.
Typical components of IPO include:
Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.
Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.
Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.
However:
Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.
Therefore, the correct answer is Option B (Business Metamorphosis).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Internal Process Optimization.
NEW QUESTION # 33
Which of the following is NOT a cluster management tool?
- A. Zettaset Orchestrator
- B. Apache Ambari
- C. Apache Hadoop
- D. Apache Mesos
Answer: C
Explanation:
Cluster management tools help in orchestrating and monitoring large-scale distributed computing environments.
Zettaset Orchestrator (A): Commercial tool for Hadoop cluster management.
Apache Mesos (B): A cluster manager that abstracts CPU, memory, and storage to enable fault-tolerant distributed systems.
Apache Ambari (C): An open-source tool for provisioning, managing, and monitoring Hadoop clusters.
Apache Hadoop (D): Not a cluster management tool. Hadoop is a framework for distributed storage and processing (HDFS + MapReduce), not a management tool.
Thus, the correct answer is Option D (Apache Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Hadoop Tools & Cluster Management.
NEW QUESTION # 34
Machine learning can be categorized as:
- A. Supervised learning
- B. All of the above
- C. Reinforcement learning
- D. Unsupervised learning
Answer: B
Explanation:
Machine learning (ML) can be broadly divided into three main paradigms:
Supervised Learning (Option A):
Data includes labeled outputs (e.g., classification, regression).
Goal: Learn a mapping from input to output.
Unsupervised Learning (Option B):
Data has no labels.
Goal: Discover hidden patterns (e.g., clustering, dimensionality reduction).
Reinforcement Learning (Option C):
Agent interacts with an environment and learns by maximizing cumulative rewards through trial and error.
Used in robotics, game AI, and autonomous systems.
Since all three categories are valid, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Paradigms: Supervised, Unsupervised, Reinforcement.
NEW QUESTION # 35
Which of the following is a "thinking like a data scientist" decomposition process?
- A. All of the above
- B. Both B and C
- C. Business Stakeholder
- D. Strategic Nouns
- E. Business Initiative
Answer: A
Explanation:
The "Thinking Like a Data Scientist" (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:
Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).
Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.
Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.
Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Science Fundamentals: Thinking Like a Data Scientist Process.
NEW QUESTION # 36
Which of the following phases is NOT a Big Data Business Model Maturity Index?
- A. Business Strategy
- B. Business Optimization
- C. Data Monetization
- D. Business Metamorphosis
- E. Business Monitoring
Answer: A
Explanation:
The Big Data Business Model Maturity Index (BDBMMI) defines phases organizations pass through in leveraging data strategically:
Business Monitoring (A): Tracking metrics and reporting.
Business Insights (not listed in options but part of the framework).
Business Optimization (B): Using analytics to improve efficiency.
Data Monetization (D): Creating new revenue streams with data.
Business Metamorphosis (E): Transforming the business model through data.
Business Strategy (Option C): While strategy is essential, it is not one of the defined phases of BDBMMI.
Thus, the correct answer is Option C (Business Strategy).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Business Model Maturity Index (BDBMMI).
NEW QUESTION # 37
A workflow refers to a:
- A. Indirected acyclic graph
- B. Indirected cyclic graph
- C. Directed acyclic graph
- D. Directed cyclic graph
Answer: C
Explanation:
In data pipelines and process orchestration, a workflow is represented as a Directed Acyclic Graph (DAG):
Directed: Each edge has a direction, representing task dependencies.
Acyclic: No cycles exist; tasks must follow a sequence without looping back.
Graph: Represents tasks as nodes and dependencies as edges.
This structure is common in tools like Apache Airflow, Spark DAGs, and Hadoop MapReduce job schedulers.
Option A & B: Incorrect, as workflows cannot have cycles (would cause infinite loops).
Option D: Incorrect, because workflows are directed, not indirected.
Thus, the correct answer is Option C (Directed Acyclic Graph).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Workflow Management with DAGs.
NEW QUESTION # 38
Which of the following is used to summarize a dataset by showing the median, quantiles, and min/max values for each of the variables?
- A. Pie Charts
- B. Scatter Chart
- C. Bar Charts
- D. Histogram
- E. Box Plots
Answer: E
Explanation:
A Box Plot (also called Whisker Plot) is a visualization tool used to summarize data distribution using five- number summary:
Minimum,
First quartile (Q1),
Median (Q2),
Third quartile (Q3),
Maximum.
It also highlights outliers explicitly.
Option A (Box Plots): Correct.
Option B (Pie Charts): Show proportions, not distribution.
Option C (Histogram): Shows frequency distribution but not quartiles/median.
Option D (Scatter Chart): Used for relationships between two variables, not summary statistics.
Option E (Bar Charts): Compare categories, not statistical spread.
Thus, the correct answer is Option A (Box Plots).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization Tools: Box Plots and Statistical Summaries.
NEW QUESTION # 39
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. Both i and ii
- B. Only ii
- C. Only i
Answer: A
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 # 40
Designing an algorithm to play chess is usually an example of which type of machine learning?
- A. Supervised learning
- B. Pattern density
- C. Clustering
- D. Reinforcement learning
Answer: D
Explanation:
Chess-playing algorithms are a classic application of Reinforcement Learning (RL) in machine learning.
In RL, an agent (chess program) interacts with an environment (chessboard/game state).
It learns optimal strategies (policies) by trial and error, guided by reward signals (e.g., winning the game, capturing pieces).
Famous examples include DeepMind's AlphaZero and earlier systems like IBM's Deep Blue, which incorporated reinforcement principles along with heuristics.
Option B (Pattern density): Not a recognized ML paradigm.
Option C (Supervised learning): While supervised ML can be used to predict moves from labeled games, chess strategy learning is best modeled as reinforcement learning.
Option D (Clustering): Not applicable; clustering is unsupervised grouping of data.
Thus, chess-playing algorithms are best categorized as Reinforcement Learning # Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Reinforcement Learning Applications: Games & Autonomous Systems.
NEW QUESTION # 41
What is Scrumban?
- A. It is Kanban
- B. It is Scrum
- C. It combines the principles of Scrum and Kanban into a pull-based system
- D. It combines the principles of Scrum and Kanban into a push-based system
Answer: C
Explanation:
Scrumban is a hybrid Agile methodology that merges Scrum and Kanban to take advantage of the strengths of both.
From Scrum, Scrumban adopts structured sprint planning, roles, and iterative review cycles.
From Kanban, it borrows the visual board system, continuous workflow management, and the pull-based approach, where tasks are pulled into the workflow only when capacity is available.
The pull-based system ensures that teams do not overload themselves and helps manage work-in-progress (WIP) effectively. This makes Scrumban particularly suitable for projects with frequent changes, ongoing maintenance tasks, or teams transitioning from Scrum to Kanban.
Thus, the correct answer is Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Project Management Techniques for Data Science.
NEW QUESTION # 42
ARIMA model is:
- A. All of the above
- B. Autoregressive moving average
- C. Autoresponsive moving average
- D. Autoreactive moving average
- E. Autointeractive moving average
Answer: B
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 # 43
Which of the following is the most important part of Hadoop?
- A. Hadoop Distributed File System (HDFS)
- B. Both B and C
- C. Spark Framework
- D. Both A and B
- E. MapReduce Framework
Answer: D
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 # 44
Which of the following is TRUE for data lake?
- A. The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
- B. The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive
- C. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized
- 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: A,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 # 45
Which of the following is a useful feature of functional programming?
- A. Higher-Order Functions (HOFs)
- B. All of the above
- C. Lazy Evaluation
- D. Immutable Data
Answer: B
Explanation:
Functional programming (FP) is a paradigm widely adopted in data science and big data tools (e.g., Spark with Scala/Python). Its useful features include:
Option A (Higher-Order Functions): Functions can take other functions as arguments or return them, enabling powerful abstractions like map(), reduce(), and filter().
Option B (Immutable Data): Ensures reliability and thread-safety, crucial for distributed computing. Once created, data structures cannot be modified, preventing side effects.
Option C (Lazy Evaluation): Computations are delayed until results are needed, improving performance in large-scale data operations.
Since FP leverages all three features, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Functional Programming in Data Science Tools.
NEW QUESTION # 46
Example of amortized performance is:
- A. All of the above
- B. HDFS dictionaries
- C. Python dictionaries
- D. Hadoop dictionaries
- E. MapReduce dictionaries
Answer: C
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 # 47
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