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NEW QUESTION # 10
The DevOps movement is an outgrowth of which of the following software development methodologies?
- A. Test-driven development and model-driven development
- B. Agile
- C. Promise-based algorithms
- D. Waterfall
Answer: B
Explanation:
The DevOps movement evolved as a natural extension of the Agile methodology.
Agile (Option A): Agile emphasizes iterative development, collaboration, and flexibility. While Agile improved software development speed, it created challenges in integrating development with IT operations.
DevOps emerged to address this by bringing operations into the Agile cycle - enabling continuous integration, delivery, and deployment.
Waterfall (Option B): Incorrect. Waterfall is a rigid, sequential methodology, fundamentally opposite to the DevOps philosophy.
Promise-based algorithms (Option C): Not a methodology - irrelevant here.
Test-driven development and model-driven development (Option D): While these practices support DevOps, they are not the origin of the movement.
Thus, the DevOps movement is an outgrowth of Agile methodology.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile and DevOps in Data Science Projects.
NEW QUESTION # 11
Which of the following is NOT used to implement Agile?
- A. Scrum
- B. Kanban
- C. Extreme Programming (XP)
- D. Six Sigma
Answer: D
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 # 12
Which of the following is NOT an example of the applications of neural networks?
- A. Traveling salesman's problem
- B. Image compression
- C. Character recognition
- D. None of the above
- E. Stock market prediction
Answer: A
Explanation:
Neural networks have been widely applied in various domains:
Option A (Character recognition): Correct application - neural networks are highly effective for OCR (Optical Character Recognition).
Option B (Stock market prediction): Correct application - neural networks are used to model time-series and nonlinear patterns in finance.
Option D (Image compression): Correct application - neural nets (autoencoders) are used for dimensionality reduction and compression.
Option C (Traveling salesman's problem): NOT a typical neural network application. This is a combinatorial optimization problem usually solved with heuristics, dynamic programming, or optimization algorithms (not standard neural networks).
Thus, the correct answer is Option C (Traveling salesman's problem).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Applications of Neural Networks.
NEW QUESTION # 13
Which of the following is TRUE for Tensor?
- A. Both B and C
- B. In Tensor, there can be arbitrarily many dimensions to the array
- C. Tensor is an array of floating-point numbers
- D. Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
- E. All of the above
Answer: E
Explanation:
A Tensor is a fundamental data structure in modern machine learning frameworks (e.g., TensorFlow, PyTorch). It is best described as a generalization of vectors and matrices to potentially higher dimensions.
Option A: Correct. Tensors typically store numeric values (commonly floating-point numbers) in structured formats.
Option B: Correct. A tensor can have any number of dimensions (rank). For example:
A scalar is a 0-D tensor.
A vector is a 1-D tensor.
A matrix is a 2-D tensor.
Higher-rank tensors can represent images, videos, or multidimensional datasets.
Option C: Correct. Tensors are explicitly designed to allow linear algebra operations, which are the foundation of deep learning computations (matrix multiplications, dot products, etc.).
Therefore, since all three statements are true, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning, Deep Learning Concepts; Official DASCA Study Guide.
NEW QUESTION # 14
Which of the following errors refers to the wrong negation of a true null hypothesis?
- A. Hypothesis Error
- B. Type I Error
- C. Logical Error
- D. None of the above
- E. Type II Error
Answer: B
Explanation:
In hypothesis testing, two main types of errors are defined:
Type I Error (Option A): Occurs when the null hypothesis (H#) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.
Type II Error (Option B): Occurs when the null hypothesis (H#) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.
Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.
Thus, the "wrong negation of a true null hypothesis" refers to a Type I Error (false positive).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Foundations in Data Science: Hypothesis Testing & Errors.
NEW QUESTION # 15
Which of the following is correct?
i. LaTeX is used to publish work in a scientific journal
ii. LaTeX is a markup language that can be compiled into formatted documents iii. LaTeX is for publishing scientific papers
- A. i, ii
- B. ii, iii
- C. i, ii, iii
- D. i, iii
Answer: C
Explanation:
LaTeX is a high-quality typesetting system widely used in academia, particularly in scientific publishing.
Statement i: Correct. LaTeX is widely used to prepare manuscripts for scientific journals, theses, and technical reports.
Statement ii: Correct. LaTeX is a markup language (similar to HTML in concept) that compiles into formatted PDFs/documents.
Statement iii: Correct. LaTeX is a standard for publishing scientific papers due to its ability to handle complex mathematical equations, references, and formatting.
Thus, all three statements are true # Option B (i, ii, iii).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools for Data Science: LaTeX for Scientific Documentation.
NEW QUESTION # 16
In regression, the principle of machine learning is used to optimize the parameters to:
- A. Both A and B
- B. Calculate the closest possible outcomes
- C. Minimize the approximation error
- 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 # 17
Business Intelligence (BI) is:
- A. BI focuses on reporting on the future state of the business
- B. Both B and C
- C. Both A and B
- D. BI focuses on descriptive analytics
- E. BI focuses on "What happened?"
Answer: C
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 # 18
IoT is built on:
- A. Cloud Computing
- B. Networks of data gathering devices
- 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 # 19
Spark should be used when:
- A. Data is not massive
- B. Both A and B
- 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 # 20
What is Scrum?
- A. Agile is a subset of Scrum
- B. Scrum and Agile are the same
- C. None of the above
- D. Scrum is a subset of Agile
Answer: D
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 # 21
Which of the following is TRUE about monetization exercise?
- A. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
- B. Both B and C
- C. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
- D. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
- E. All of the above
Answer: E
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 # 22
The spokes of the "Hub and Spoke" analytics architecture are the analytic use cases or applications that help the organization to optimize:
- A. Deliver a more compelling customer experience
- B. Both A and B
- C. Key business processes
- D. Uncover new monetization opportunities
- E. All of the above
Answer: E
Explanation:
In the Hub and Spoke analytics architecture:
The hub is the central data platform (data lake, warehouse, or unified data hub).
The spokes are the analytic use cases or applications that leverage this data to create business value.
These spokes typically help the organization:
Optimize key business processes (Option A).
Deliver improved customer experiences (Option B).
Uncover monetization opportunities (Option C).
Since all three are valid, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Hub-and-Spoke Analytics.
NEW QUESTION # 23
HDFS supports which quotas?
- A. Both A and B
- B. None of the above
- C. Name quotas
- D. Space quotas
Answer: A
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 # 24
SpamAssassin has been developed to detect:
- A. Spam emails
- B. None of the above
- C. Email with big attachments
- D. Email with virus
Answer: A
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 # 25
Data wrangling is the process of getting the data from:
- A. Both A and B
- B. None of the above
- C. Its modified meaning format into something suitable for more conventional analytics
- D. Its raw format into something suitable for more conventional analytics
Answer: D
Explanation:
Data wrangling (also called data munging) refers to transforming raw, messy, or unstructured data into a clean and structured format suitable for analysis.
Option A: Correct. Raw data often contains missing values, duplicates, or irregular formats. Wrangling prepares it for conventional analytics and machine learning.
Option B: Incorrect. Wrangling does not involve "modified meaning"; it focuses on cleaning, structuring, and integrating.
Option C: Incorrect, since only A is correct.
Option D: Incorrect, because wrangling is explicitly described in A.
Thus, the correct answer is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Practices: Data Wrangling & Preprocessing.
NEW QUESTION # 26
Which of these statements reflects a null hypothesis?
- A. Women will score higher than men on empathy
- B. There will be no relationship between caffeine consumption and performance
- C. As temperature increases, so too will the level of aggression
- D. There will be a significant difference between group 1 and group 2
- E. Men will score higher than women on spatial awareness
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
Which of the following is an example of graphical model?
- A. Bayesian Networks
- B. Both A and C
- C. Both A and B
- D. Geographical Networks
- E. Markov Random Fields
Answer: C
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 # 28
Example of amortized performance is:
- A. MapReduce dictionaries
- B. All of the above
- C. HDFS dictionaries
- D. Python dictionaries
- E. Hadoop dictionaries
Answer: D
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 # 29
In unsupervised learning, learning takes place by based on these deductions in input data and developing patterns:
- A. Both A and B
- B. None of the above
- C. Detecting regularities
- D. Detecting irregularities
Answer: A
Explanation:
Unsupervised learning is a machine learning approach where no labeled outputs are provided. The algorithm discovers patterns or structures directly from raw data.
Option A (Detecting regularities): Correct. Unsupervised learning identifies hidden structures such as clusters, associations, and dimensionality reductions (e.g., k-means clustering, PCA).
Option B (Detecting irregularities): Correct. Outlier detection is also a part of unsupervised learning, often used in anomaly detection (e.g., fraud detection, intrusion detection).
Option C: Correct, since unsupervised learning helps detect both regularities (clusters, groups) and irregularities (outliers, anomalies).
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Unsupervised Learning: Clustering, Anomaly Detection, and Pattern Discovery.
NEW QUESTION # 30
Which of the following is correct about microservices?
- A. Each service can be developed in any language that best fits the requirement
- B. Each service is independent
- 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 # 31
Machine learning can be used in:
- A. Web search results
- B. Real-time ads on web pages and mobile devices
- C. Pattern and image recognition
- D. Fraud detection
- 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 # 32
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 # 33
OCR (Optical Character Recognition) is an application used for:
- A. Data mining
- B. Big Data Analytics
- C. MapReduce
- 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 # 34
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