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NEW QUESTION # 46
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. Histogram
- B. Bar Charts
- C. Scatter Chart
- D. Pie Charts
- 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 # 47
What is DevOps?
- A. Quality Assurance
- B. All
- C. Software Operations
- D. Software Development
Answer: B
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 # 48
Which of the following is TRUE for Business Metamorphosis?
- A. 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
- B. 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
- C. Both A and C
- 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. All of the above
Answer: E
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 # 49
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
- A. None of the above
- B. Trend analysis
- C. High Curve Equivalent (HCE)
- D. Normal Curve Equivalent (NCE)
- E. Medium Curve Equivalent (MCE)
Answer: D
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 # 50
ARIMA model is:
- A. Autoreactive moving average
- B. Autoresponsive moving average
- C. Autoregressive moving average
- D. All of the above
- E. Autointeractive moving average
Answer: C
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 # 51
Machine learning can be used in:
- A. Real-time ads on web pages and mobile devices
- B. Fraud detection
- C. Pattern and image recognition
- D. Web search results
- E. All of the above
Answer: E
Explanation:
Machine Learning has broad applications across industries and technologies:
Fraud Detection (Option A): Detecting anomalies in financial transactions, credit card usage, and cybersecurity threats.
Web Search Results (Option B): Ranking algorithms (e.g., Google's PageRank enhanced by ML techniques) improve relevance of search queries.
Real-time Ads (Option C): Online ad systems use reinforcement learning and recommendation models to target ads dynamically.
Pattern & Image Recognition (Option D): ML (especially deep learning) powers facial recognition, handwriting recognition, medical imaging, etc.
Since ML is used in all these applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning Across Domains.
NEW QUESTION # 52
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. A Markov chain is the state of a system at sequential points in time
- D. Both A and B
- E. Both B and C
Answer: E
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 # 53
Spark is written in:
- A. Python
- B. C++
- C. Scala
- D. Java
- E. C
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 # 54
OCR (Optical Character Recognition) is an application used for:
- A. Big Data Analytics
- B. MapReduce
- C. Data mining
- D. Machine learning
Answer: D
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 # 55
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. Histogram
- B. Trend Analysis
- C. None of the above
- D. Scatter Plot
- E. Box Plots
Answer: E
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 # 56
Which of these are open-source column-oriented databases?
- A. Cassandra
- B. Accumulo
- C. Both A and B
- D. HBase
- E. All of the above
Answer: E
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 # 57
Which of the following is FALSE for Social Network Analysis (SNA)?
- A. SNA is used to investigate social structures and relationships across social networks
- B. None of the above
- C. Social Network Analysis (SNA) is an example of graph analysis
- D. Social Network Analysis (SNA) is an example of trend analysis
- E. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
Answer: D
Explanation:
Social Network Analysis (SNA) is a powerful analytical method that applies graph theory to study relationships among entities (people, organizations, computers, etc.).
Option A: Correct. SNA is indeed an example of graph analysis because it models entities as nodes and their relationships as edges/ties.
Option B: FALSE. SNA is not an example of trend analysis. Trend analysis focuses on temporal patterns (time series), while SNA is structural and relational.
Option C: Correct. SNA investigates structures such as communities, influencers, and information diffusion in networks.
Option D: Correct. The characterization of nodes and edges is central to SNA.
Option E: Incorrect, since we've identified Option B as false.
Thus, the false statement is Option B.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graph Analysis & Social Network Analysis.
NEW QUESTION # 58
IoT is built on:
- A. Networks of data gathering devices
- B. None of the above
- C. Cloud Computing
- D. Both A and B
Answer: D
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 # 59
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 # 60
Which of the following is NOT a cluster management tool?
- A. Apache Mesos
- B. Apache Hadoop
- C. Zettaset Orchestrator
- D. Apache Ambari
Answer: B
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 # 61
Which of the following is a "thinking like a data scientist" decomposition process?
- A. Both B and C
- B. Business Stakeholder
- C. Strategic Nouns
- D. Business Initiative
- E. All of the above
Answer: E
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 # 62
What is Scrum?
- A. Scrum is a subset of Agile
- B. None of the above
- C. Agile is a subset of Scrum
- D. Scrum and Agile are the same
Answer: A
Explanation:
Scrum is a framework used to implement Agile principles. Agile itself is the overarching philosophy or mindset, while Scrum is one of the most popular frameworks that apply Agile values in practice.
Option A (Correct): Scrum is indeed a subset of Agile. Agile defines the principles (from the Agile Manifesto), and Scrum provides the structure (roles, artifacts, ceremonies).
Option B: Incorrect. Agile is broader and not a subset of Scrum.
Option C: Incorrect. Scrum and Agile are not the same; Agile is the philosophy, Scrum is a methodology under Agile.
Option D: Incorrect because Option A is valid.
Thus, the correct answer is Option A: Scrum is a subset of Agile.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile and Scrum in Data Science Projects.
NEW QUESTION # 63
HDFS supports which quotas?
- A. Space quotas
- B. None of the above
- C. Name quotas
- D. Both A and B
Answer: D
Explanation:
HDFS (Hadoop Distributed File System) provides quota management to control and monitor resource usage across directories:
Name Quotas (Option A): Limits the number of files and directories that can be created in a given HDFS directory. Helps prevent excessive metadata growth.
Space Quotas (Option B): Limits the total disk space consumed by files within a directory. Helps in capacity planning and avoiding storage overuse.
Since HDFS supports both types, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: HDFS Management and Quotas.
NEW QUESTION # 64
Exploratory analytic algorithms help the Data Science team to better:
- A. Gain a high-level understanding of relationships
- B. Understand the data content
- C. Understand patterns in the data
- D. Both A and B
- E. All of the above
Answer: E
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 # 65
JSON takes hierarchical data structures and serializes them into:
- A. Plain string format
- B. Plain text format
- C. Any desired format
- D. None of the above
- E. Both A and B
Answer: E
Explanation:
JSON (JavaScript Object Notation) is a lightweight data-interchange format widely used for storing and exchanging structured or semi-structured data. JSON allows hierarchical (tree-like) structures, such as nested objects and arrays, to be serialized into a textual representation.
Option A (Plain text format): Correct. JSON files are stored as plain text, making them human-readable and language-independent.
Option B (Plain string format): Correct. JSON objects are transmitted as strings across networks (e.g., via APIs, RESTful services).
Option C: Incorrect. JSON does not serialize into "any format," but specifically into text/string-based formats.
Option D: Correct. Since JSON is both plain text and transmitted as string format, the right answer is both A and B.
Option E: Incorrect.
Thus, JSON serializes hierarchical data into plain text and string formats.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Tools: Data Serialization Formats (JSON, XML, Avro).
NEW QUESTION # 66
Which classification steps are performed in inductive techniques?
i. Training Step
ii. Test Step
iii. Validation Step
iv. Application Step
- A. i, ii, iv
- B. i, ii
- C. i, ii, iii, iv
- D. ii, iii
Answer: C
Explanation:
Inductive learning techniques in machine learning (such as decision trees, neural networks, or SVMs) follow a systematic sequence of steps for classification:
Training Step (i): A model is built using training data, where the system learns relationships between features and target labels.
Test Step (ii): The trained model is evaluated on unseen test data to measure its performance and generalizability.
Validation Step (iii): Often, a validation set is used to fine-tune model parameters, avoid overfitting, and choose the best model configuration.
Application Step (iv): The final validated model is applied to classify new, real-world data.
Since all four steps (i, ii, iii, iv) are essential to inductive classification, the correct answer is Option D (i, ii, iii, iv).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics & Machine Learning: Classification and Inductive Learning Techniques.
NEW QUESTION # 67
SpamAssassin has been developed to detect:
- A. Email with big attachments
- B. Spam emails
- C. None of the above
- D. Email with virus
Answer: B
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 # 68
Which of the following is NOT an example of graphical model?
- A. Geographical networks
- B. Road maps
- C. Computer networks
- D. Flow charts
- E. Electrical circuits
Answer: D
Explanation:
Graphical models represent relationships between objects using nodes (entities) and edges (relationships).
Examples include:
Road maps (Option A): Nodes = intersections, Edges = roads.
Electrical circuits (Option B): Nodes = components, Edges = connections.
Computer networks (Option C): Nodes = devices, Edges = connections.
Geographical networks (Option D): Nodes = locations, Edges = transport or connectivity.
However:
Flow charts (Option E): These represent process flows, not structural networks of entities and relationships.
They are procedural diagrams, not graphical models in the statistical/graph-theory sense.
Thus, the correct answer is Option E (Flow charts).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graphical Models and Graph Analysis.
NEW QUESTION # 69
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