Free DASCA Data Scientist SDS Ultimate Study Guide (Updated 87 Questions)

Get to the Top with SDS Practice Exam Questions

NEW QUESTION # 46
In regression, the principle of machine learning is used to optimize the parameters to:

• A. None of the above
• B. Minimize the approximation error
• C. Both A and B
• D. Calculate the closest possible outcomes

Answer: C

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 # 47
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 # 48
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. None of the above
• B. Scatter Plot
• C. Trend Analysis
• D. Histogram
• 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 # 49
Self-driving car is an example of:

• A. Unsupervised learning
• B. Reinforcement learning
• C. All of the above
• D. Supervised learning

Answer: B

Explanation:
Self-driving cars (autonomous vehicles) are an application of Reinforcement Learning (RL) in machine learning:
In RL, an agent (car) interacts with an environment (roads, obstacles, traffic) and learns to maximize rewards (e.g., safe driving, efficient navigation).
The system improves performance through trial-and-error learning, guided by reward signals such as staying in a lane or avoiding collisions.
Supervised learning (A): Used in some supporting tasks like image recognition (e.g., identifying stop signs), but not the core paradigm for self-driving.
Unsupervised learning (B): Useful for clustering sensor data, but again not the main paradigm.
Reinforcement learning (C): Correct, since self-driving fundamentally depends on RL decision-making.
Thus, the correct answer is Option C (Reinforcement Learning).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Paradigms: Reinforcement Learning and Autonomous Systems.

NEW QUESTION # 50
SpamAssassin has been developed to detect:

• A. None of the above
• B. Email with virus
• C. Spam emails
• D. Email with big attachments

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 # 51
What is Scrumban?

• A. It is Kanban
• B. It combines the principles of Scrum and Kanban into a push-based system
• C. It combines the principles of Scrum and Kanban into a pull-based system
• D. It is Scrum

Answer: C

Explanation:
Scrumban is a hybrid Agile methodology that merges Scrum and Kanban to take advantage of the strengths of both.
From Scrum, Scrumban adopts structured sprint planning, roles, and iterative review cycles.
From Kanban, it borrows the visual board system, continuous workflow management, and the pull-based approach, where tasks are pulled into the workflow only when capacity is available.
The pull-based system ensures that teams do not overload themselves and helps manage work-in-progress (WIP) effectively. This makes Scrumban particularly suitable for projects with frequent changes, ongoing maintenance tasks, or teams transitioning from Scrum to Kanban.
Thus, the correct answer is Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Project Management Techniques for Data Science.

NEW QUESTION # 52
Which of the following is True about Time Series Analysis?

• A. Both A and B
• B. Predicting when/whether an event will occur, such as a failure of the machine generating the data
• C. Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through
• D. Projecting the value of the time series at future points in time, such as a stock whose price we want to predict
• E. All of the above

Answer: E

Explanation:
Time Series Analysis (TSA) is the process of analyzing data collected sequentially over time to extract meaningful insights.Applications include:
Option A: Correct. Event prediction (e.g., failure detection in IoT or predictive maintenance).
Option B: Correct. Forecasting future values (e.g., stock price, sales forecasting).
Option C: Correct. Pattern discovery in large-scale time series datasets using clustering, anomaly detection, or seasonality detection.
Since all three are true, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Time Series Analysis and Forecasting.

NEW QUESTION # 53
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. Histogram
• 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 # 54
Which of the following is FALSE for Social Network Analysis (SNA)?

• A. None of the above
• B. SNA is used to investigate social structures and relationships across social networks
• C. Social Network Analysis (SNA) is an example of graph analysis
• D. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
• E. Social Network Analysis (SNA) is an example of trend analysis

Answer: E

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 # 55
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 # 56
The aim of developing Scribe was to offer reliability with using:

• A. Complex protocols
• B. High disk space usage
• C. None of the above
• D. Extensive protocols

Answer: C

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 # 57
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 clients/stakeholders need to be able to change the scope
• C. None of the above
• D. When the final product isn't clearly defined

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 # 58
Which of the following is the most important part of Hadoop?

• A. MapReduce Framework
• B. Both B and C
• C. Both A and B
• D. Hadoop Distributed File System (HDFS)
• E. Spark Framework

Answer: C

Explanation:
The Hadoop ecosystem consists of multiple components, but the two core components that define Hadoop are:
HDFS (Hadoop Distributed File System): Provides fault-tolerant, scalable storage across distributed clusters.
It is the backbone for storing massive datasets in a distributed fashion.
MapReduce Framework: Provides the parallel computing and data processing layer in Hadoop, enabling batch analysis over distributed datasets.
Option A: Correct, HDFS is essential.
Option B: Correct, MapReduce is essential.
Option C: Incorrect, Spark is a newer processing framework, but it is not originally part of Hadoop core.
Option D: Correct answer since both HDFS and MapReduce are considered the fundamental parts of Hadoop.
Option E: Incorrect, because Spark is not a core Hadoop component (though it integrates with Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystems: Hadoop Architecture & Components.

NEW QUESTION # 59
Spark should be used when:

• A. Data is massive
• B. None of the above
• C. Both A and B
• D. Data is not massive

Answer: A

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 # 60
Data wrangling is the process of getting the data from:

• A. None of the above
• B. Both A and B
• 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 # 61
Which of the following architectural techniques is used for parallel processing?

• A. Both B and C
• B. Both A and B
• C. Very Long Instruction Words (VLIW) Technique
• D. The Superscalar Technique
• E. The SuperVector Technique

Answer: B

Explanation:
Parallel processing architectures are designed to execute multiple instructions or operations simultaneously:
Superscalar Technique (Option A): Uses multiple execution units so that several instructions can be issued and executed in parallel within a single CPU cycle.
VLIW Technique (Option B): Uses very long instruction words, where multiple operations are encoded into a single instruction and executed in parallel.
SuperVector (Option C): Refers to vector processors, which process large arrays of data but is not classified as a mainstream architectural parallel technique in modern CPU design.
Therefore, the primary architectural techniques for parallel processing are Superscalar and VLIW, making Option D (Both A and B) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Parallel and Distributed Processing.

NEW QUESTION # 62
Which of these statements reflects a null hypothesis?

• A. There will be a significant difference between group 1 and group 2
• B. As temperature increases, so too will the level of aggression
• C. Women will score higher than men on empathy
• D. Men will score higher than women on spatial awareness
• E. There will be no relationship between caffeine consumption and performance

Answer: E

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 # 63
A burn down chart shows:

• A. The volume of work and features completed
• B. The number of hours worked after dark
• C. The declining energy of the team
• D. The rate of reduction of budget for a project

Answer: A

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 # 64
Which of the following is a Python library for fitting Bayesian networks to real data?

• A. SCIMC
• B. MCMC
• C. MyLib
• D. PyMC
• E. SciLib

Answer: D

Explanation:
The correct answer isPyMC(Option B).
PyMC is an open-source Python library widely used forBayesian statistical modelingandprobabilistic machine learning. It provides a robust framework for defining and fitting Bayesian networks to real data usingMarkov Chain Monte Carlo (MCMC)sampling techniques, as well asvariational inferencemethods.
This makes it a powerful tool for data scientists who want to work withuncertainty modeling,probabilistic inference, andcausal reasoningin complex datasets.
Let's clarify the other options to avoid confusion:
* Option A: SciLib- There is no standard Python library by this name that is related to Bayesian networks. (It may be confused withSciPyorSciKit-Learn, but those are not specialized for Bayesian inference.)
* Option C: MyLib- This is not a recognized Python package in the data science ecosystem.
* Option D: MCMC- While Markov Chain Monte Carlo is thetechniqueused in Bayesian estimation, it is not a standalone library. Instead, PyMC implements MCMC as part of its computational framework.
* Option E: SCIMC- No such Python library exists; it appears to be a distractor.
PyMC's primary strength is its ability to let data scientists define models in aprobabilistic programming style, making it easier to represent uncertainties and hidden variables in data. This aligns with DASCA's emphasis on ensuring data scientists understand bothstatistical foundationsand thetools required to implement them programmatically.
In practice, PyMC is often used in applications such as:
* Forecasting(e.g., time series with uncertainty bounds)
* Causal inference(estimating hidden relationships in data)
* Risk modeling(finance, healthcare, or supply chain domains)
* Machine learning with uncertainty quantification
Thus,PyMCis the correct library for fitting Bayesian networks in Python.
Reference:DASCA Data Scientist Knowledge Framework (DSKF) -Programming for Data Science & Probabilistic Modeling Tools, Official DASCA Study Guide.

NEW QUESTION # 65
What is DevOps?

• A. Software Operations
• B. Quality Assurance
• C. Software Development
• D. All

Answer: D

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 # 66
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