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NEW QUESTION # 14
Which of the following is NOT a correct situation to use Agile?
- A. When changes need to be implemented during the entire process
- B. When the final product isn't clearly defined
- C. None of the above
- D. When clients/stakeholders need to be able to change the scope
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 # 15
Which of the following is TRUE for Business Metamorphosis?
- A. 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
- B. Both A and C
- C. All of the above
- D. 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
- 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: C
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 # 16
Machine learning can be categorized as:
- A. All of the above
- B. Reinforcement learning
- C. Unsupervised learning
- D. Supervised learning
Answer: A
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 # 17
Which of the following statements is correct?
- A. 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
- B. 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
- C. 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
- D. 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
Answer: A
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 # 18
What is the agenda of discussion at a "stand up" meeting of an Agile team?
- A. Both A and B
- B. All of the above
- C. What they are planning to do today
- D. What they accomplished the previous day
- E. Any roadblocks they are running into
Answer: B
Explanation:
A daily stand-up meeting (also called a daily Scrum) is a short meeting (usually 15 minutes) that Agile teams hold to synchronize progress. Its agenda is structured around three key questions:
What was accomplished yesterday? (Progress review).
What is planned for today? (Work alignment).
What impediments or roadblocks exist? (Barriers identification).
This process enhances transparency, communication, and accountability, ensuring the team can quickly address obstacles and stay aligned with sprint goals.
Option A: Correct - yesterday's work is discussed.
Option B: Correct - today's planned tasks are outlined.
Option C: Correct - roadblocks are highlighted.
Option D: Incomplete since it misses C.
Option E: Correct - covers all agenda items.
Thus, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Practices in Data Science Projects.
NEW QUESTION # 19
Which of the following is NOT a cluster management tool?
- A. Apache Mesos
- B. Apache Ambari
- C. Zettaset Orchestrator
- D. Apache Hadoop
Answer: D
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 # 20
Designing an algorithm to play chess is usually an example of which type of machine learning?
- A. Reinforcement learning
- B. Clustering
- C. Pattern density
- D. Supervised learning
Answer: A
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 # 21
Which of the following is a trend analysis component of time series decomposition?
- A. Both A and B
- B. Cyclical
- C. All of the above
- D. Seasonal
- E. Irregular
Answer: C
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 # 22
A workflow refers to a:
- A. Indirected cyclic graph
- B. Indirected acyclic graph
- C. Directed cyclic graph
- D. Directed acyclic graph
Answer: D
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 # 23
Business Intelligence (BI) is:
- A. Both A and B
- B. BI focuses on "What happened?"
- C. Both B and C
- D. BI focuses on reporting on the future state of the business
- E. BI focuses on descriptive analytics
Answer: A
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 # 24
Which of the following phases is NOT a Big Data Business Model Maturity Index?
- A. Business Metamorphosis
- B. Business Optimization
- C. Business Strategy
- D. Business Monitoring
- E. Data Monetization
Answer: C
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 # 25
Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?
- A. Trend Analysis
- B. Histogram
- C. Scatter Plot
- D. Box Plots
- E. None of the above
Answer: D
Explanation:
Box plots (or Whisker plots) are statistical graphics that represent data distribution through:
Minimum, First Quartile (Q1), Median, Third Quartile (Q3), and Maximum.
Outliers are plotted as individual points beyond the whiskers.
This makes them particularly powerful for:
Identifying outliers in data.
Comparing distributions across categories.
Understanding variability in data.
Option A (Trend Analysis): Shows temporal patterns, not individual outliers.
Option C (Histogram): Shows frequency distribution but does not explicitly highlight outliers.
Option D (Scatter Plot): Shows relationships between variables but doesn't focus on statistical outliers in one distribution.
Thus, the correct answer is Option B (Box Plots).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization Tools: Box Plots for Outlier Detection.
NEW QUESTION # 26
Which of these statements reflects a null hypothesis?
- A. As temperature increases, so too will the level of aggression
- B. There will be no relationship between caffeine consumption and performance
- C. There will be a significant difference between group 1 and group 2
- D. Men will score higher than women on spatial awareness
- E. Women will score higher than men on empathy
Answer: B
Explanation:
A null hypothesis (H#) is the default assumption in statistical testing that there is no effect, no difference, or no relationship between variables.
Option A: Correct. This states explicitly that there is no relationship between caffeine consumption and performance # fits the definition of a null hypothesis.
Options B, C, D, E: These all hypothesize differences or relationships # they are examples of alternative hypotheses (H#), not null.
Thus, the correct answer is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistics in Data Science: Hypotheses, Errors, and Testing.
NEW QUESTION # 27
In regression, the principle of machine learning is used to optimize the parameters to:
- A. Both A and B
- B. Minimize the approximation error
- C. Calculate the closest possible outcomes
- D. None of the above
Answer: A
Explanation:
Regression is a supervised learning technique where a model estimates the relationship between input features (independent variables) and an output (dependent variable).
Option A: Correct. The learning process involves optimizing model parameters (e.g., coefficients in linear regression) to minimize approximation error. Common loss functions include Mean Squared Error (MSE) or Mean Absolute Error (MAE).
Option B: Correct. Minimizing error enables the model to produce the closest possible outcomes to the actual observed values, ensuring accurate predictions.
Option C: Correct, since both A and B are true.
Option D: Incorrect.
Thus, regression optimization in machine learning aims to minimize approximation error and generate closest possible outcomes, making Option C the correct answer.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics & Machine Learning: Regression Models and Optimization Principles.
NEW QUESTION # 28
IoT is built on:
- A. Both A and B
- B. Networks of data gathering devices
- C. Cloud Computing
- D. None of the above
Answer: A
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 # 29
Spark should be used when:
- A. Both A and B
- B. Data is not massive
- C. Data is massive
- D. None of the above
Answer: C
Explanation:
Apache Spark is a distributed data processing engine optimized for big data scenarios. It is specifically designed to handle:
Large-scale datasets spread across clusters.
Massive streaming or batch data pipelines.
Machine learning and graph processing at scale.
Option A: Correct - Spark excels when data is massive and distributed.
Option B: Incorrect - Spark is overkill for small data (Pandas, NumPy, or scikit-learn would be more efficient).
Option C: Incorrect - Spark is not optimized for small datasets.
Option D: Incorrect - since A is valid.
Thus, Spark should be used when data is massive # Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Processing: Apache Spark Applications.
NEW QUESTION # 30
SpamAssassin has been developed to detect:
- A. Email with virus
- B. Email with big attachments
- C. Spam emails
- D. None of the above
Answer: C
Explanation:
Apache SpamAssassin is one of the most widely used open-source tools for spam email detection.
It applies a rule-based system combined with Bayesian filtering, heuristics, and collaborative filtering methods to classify incoming emails as spam or legitimate.
Option A (Spam emails): Correct, this is the main function.
Option B (Big attachments): Incorrect. Large attachment filtering is not its primary purpose.
Option C (Email with virus): Incorrect. That falls under antivirus or malware detection tools, not SpamAssassin.
Option D: Incorrect since A is valid.
Thus, the correct answer is Option A (Spam emails).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Email Filtering and Text Mining.
NEW QUESTION # 31
Which of the following is correct about microservices?
- A. Each service is independent
- B. Each service can be developed in any language that best fits the requirement
- C. All of the above
- D. Each service is a new project
Answer: C
Explanation:
Microservices architecture is a software design approach where applications are developed as a collection of small, loosely coupled, and independently deployable services.
Option A: Correct. Each microservice runs independently and has its own lifecycle, allowing scalability and modularity.
Option B: Correct. Each service can be treated as an independent project with its own repository, build pipeline, and deployment.
Option C: Correct. Microservices can be developed in different programming languages or frameworks, chosen based on the best fit for each specific requirement.
Therefore, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Management & Engineering: Microservices in Data Ecosystems.
NEW QUESTION # 32
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