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NEW QUESTION # 31
Which of the following is NOT used to implement Agile?
- A. Extreme Programming (XP)
- B. Scrum
- C. Six Sigma
- D. Kanban
Answer: C
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 # 32
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. Scatter Plot
- B. Trend Analysis
- C. None of the above
- D. Box Plots
- E. Histogram
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 # 33
Which of the following is the common evolutionary algorithm of neural networks?
- A. All of the above
- B. Evolution strategy
- C. Genetic programming
- D. Genetic algorithm
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 # 34
Which of the following statements is correct?
- A. 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
- B. 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
- C. 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
- 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: B
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 # 35
HDFS supports which quotas?
- A. Space quotas
- B. None of the above
- C. Both A and B
- D. Name quotas
Answer: C
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 # 36
SpamAssassin has been developed to detect:
- A. Email with virus
- B. Email with big attachments
- C. None of the above
- D. Spam emails
Answer: D
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 # 37
Which of the following is NOT Structured data?
- A. Data tables
- B. CSV files
- C. None of the above
- D. XML
- E. Relational databases
Answer: D
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 # 38
Which of the following is TRUE for Tensor?
- A. All of the above
- B. Tensor is an array of floating-point numbers
- C. In Tensor, there can be arbitrarily many dimensions to the array
- D. Both B and C
- E. Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
Answer: A
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 # 39
What is TRUE for "rehashing"?
- A. All of the above
- B. Allocate a new, larger hash table in memory
- C. Both A and B
- D. Key/value pairs from the original table can be inserted into the new, larger one
- E. It requires a new hash function, which maps values into a larger range of integers
Answer: A
Explanation:
Rehashing is a technique used in dynamic hash tables when the load factor (ratio of entries to bucket size) exceeds a certain threshold. It ensures efficient lookup, insertion, and deletion operations.
Option A (Correct): A larger hash table is allocated in memory to accommodate more entries.
Option B (Correct): A new hash function is typically required to map keys into the expanded table range.
Option C (Correct): All key-value pairs from the old table are re-inserted (rehashed) into the new table using the new hash function.
Since all three conditions (A, B, and C) are true, the best choice is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Data Structures & Hashing Techniques
NEW QUESTION # 40
Machine learning can be used in:
- A. All of the above
- B. Web search results
- C. Fraud detection
- D. Pattern and image recognition
- E. Real-time ads on web pages and mobile devices
Answer: A
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 # 41
Which of the following is NOT a part of Internal Process Optimization?
- A. None of the above
- B. Business Insights
- C. Business Monitoring
- D. Business Optimization
- E. Business Metamorphosis
Answer: E
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 # 42
Which of the following phases is NOT a Big Data Business Model Maturity Index?
- A. Business Metamorphosis
- B. Data Monetization
- C. Business Strategy
- D. Business Monitoring
- E. Business Optimization
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 # 43
Tar is an example of:
- A. None of the above
- B. Archive file format
- C. CSV file format
- D. Text file format
- E. ARV file format
Answer: B
Explanation:
TAR (Tape Archive) is a widely used archive file format in Unix/Linux environments. It is used to combine multiple files into a single archive file (with extension .tar).
Option A: Correct. TAR is specifically designed for archiving.
Option B (CSV): Incorrect. CSV (Comma-Separated Values) is a tabular text data format.
Option C (ARV): Incorrect - no such format.
Option D (Text): Incorrect. Though TAR may contain text files, the TAR format itself is not plain text but an archive format.
Option E: Incorrect since Option A is valid.
Thus, TAR is an Archive file format.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Storage Formats in Data Science & Engineering.
NEW QUESTION # 44
A burn down chart shows:
- A. The number of hours worked after dark
- B. The declining energy of the team
- C. The rate of reduction of budget for a project
- D. The volume of work and features completed
Answer: D
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 # 45
ARIMA model is:
- A. Autoreactive moving average
- B. Autointeractive moving average
- C. Autoregressive moving average
- D. Autoresponsive moving average
- E. All of the above
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 # 46
Which of the following is an example of graphical model?
- A. Both A and C
- B. Geographical Networks
- C. Bayesian Networks
- D. Both A and B
- E. Markov Random Fields
Answer: D
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 # 47
The main purpose of a Statement Of Work (SOW) is to get:
- A. All of the above
- B. What expectations are realistic
- C. None of the above
- D. Everybody on the same page about what work should be done
- E. What the priorities are
Answer: A
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 # 48
Designing an algorithm to play chess is usually an example of which type of machine learning?
- A. Supervised learning
- B. Reinforcement learning
- C. Pattern density
- D. Clustering
Answer: B
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 # 49
Which of the following is a trend analysis component of time series decomposition?
- A. All of the above
- B. Irregular
- C. Both A and B
- D. Cyclical
- E. Seasonal
Answer: A
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 # 50
OCR (Optical Character Recognition) is an application used for:
- A. Data mining
- B. Machine learning
- C. Big Data Analytics
- D. MapReduce
Answer: B
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 # 51
Which of the following is NOT an example of the applications of neural networks?
- A. None of the above
- B. Image compression
- C. Traveling salesman's problem
- D. Stock market prediction
- E. Character recognition
Answer: C
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 # 52
The DevOps movement is an outgrowth of which of the following software development methodologies?
- A. Agile
- B. Promise-based algorithms
- C. Test-driven development and model-driven development
- D. Waterfall
Answer: A
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 # 53
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. All of the above
- B. i, ii
- C. ii, iii
Answer: A
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 # 54
The aim of developing Scribe was to offer reliability with using:
- A. None of the above
- B. Complex protocols
- C. High disk space usage
- D. Extensive protocols
Answer: A
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 # 55
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. Scatter Chart
- B. Bar Charts
- C. Box Plots
- D. Histogram
- E. Pie Charts
Answer: C
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 # 56
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