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NEW QUESTION # 21
Exploratory analytic algorithms help the Data Science team to better:
- A. Understand the data content
- B. All of the above
- C. Gain a high-level understanding of relationships
- D. Understand patterns in the data
- E. Both A and B
Answer: B
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 # 22
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 # 23
Which of the following is NOT a main data container in Python?
- A. Dict
- B. LinkedList
- C. Tuples
- D. Lists
Answer: B
Explanation:
Python's built-in data containers are widely used in data science and programming:
Lists (Option A): Ordered, mutable sequences.
Tuples (Option B): Ordered, immutable sequences.
Dict (Option D): Dictionaries, key-value data structures implemented using hash tables.
LinkedList (Option C): Python does not provide a native linked list as a built-in container. While linked lists can be implemented manually or through external libraries (e.g., collections.deque for efficient appends
/pops), they are not part of Python's main native containers.
Thus, the correct answer is Option C (LinkedList).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Python Data Structures.
NEW QUESTION # 24
Spark should be used when:
- A. Data is not massive
- B. None of the above
- C. Data is massive
- D. Both A and B
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 # 25
Which of the following is FALSE for Social Network Analysis (SNA)?
- A. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
- B. SNA is used to investigate social structures and relationships across social networks
- C. Social Network Analysis (SNA) is an example of trend analysis
- D. Social Network Analysis (SNA) is an example of graph analysis
- E. None of the above
Answer: C
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 # 26
Spark is written in:
- A. Java
- B. Python
- C. Scala
- D. C++
- 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 # 27
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 # 28
Semi-structured data does NOT include:
- A. Scientific data
- B. Schema-full data
- C. File systems
- D. Database system
Answer: B
Explanation:
Semi-structured data falls between structured data (e.g., relational databases with fixed schema) and unstructured data (e.g., free text, audio, video). It typically includes irregular or flexible schema information, such as XML, JSON, email data, or log files.
Option A (Database systems): Correct, databases may hold semi-structured content (e.g., JSON or XML columns).
Option B (File systems): Correct, file-based storage (logs, JSON, Avro, CSV) often contains semi-structured data.
Option C (Scientific data): Correct, many scientific applications generate semi-structured data formats (sensor readings, genomic sequences, etc.).
Option D (Schema-full data): Correct Answer. Schema-full (strict schema-defined relational tables) represent structured data, not semi-structured.
Thus, semi-structured data does NOT include schema-full data.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Data Types & Sources.
NEW QUESTION # 29
A burn down chart shows:
- A. The rate of reduction of budget for a project
- B. The declining energy of the team
- C. The volume of work and features completed
- D. The number of hours worked after dark
Answer: C
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 # 30
Which of the following can be classified as factor analysis in machine learning?
- A. Exploratory factor analysis
- B. Confirmatory factor analysis
- C. Both A and B
- D. None of the above
Answer: C
Explanation:
Factor analysis is a dimensionality reduction technique used to uncover latent variables (factors) that explain observed patterns of correlations in data. It is widely used in psychometrics, social sciences, and machine learning.
Exploratory Factor Analysis (EFA, Option A): Used when the underlying factor structure is unknown, aiming to discover potential latent variables.
Confirmatory Factor Analysis (CFA, Option B): Used when there is a hypothesis about factor structure, and the goal is to confirm it statistically.
Both are valid approaches to factor analysis, hence the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Dimensionality Reduction & Factor Analysis in Machine Learning.
NEW QUESTION # 31
Which of the following architectural techniques is used for parallel processing?
- A. Both B and C
- B. Very Long Instruction Words (VLIW) Technique
- C. The Superscalar Technique
- D. Both A and B
- E. The SuperVector Technique
Answer: D
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 # 32
Which of these are open-source column-oriented databases?
- A. Accumulo
- B. All of the above
- C. Cassandra
- D. HBase
- E. Both A and B
Answer: B
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 # 33
Spark programs can be written in:
- A. Java
- B. All of the above
- C. Python
- D. None of the above
- E. Scala
Answer: B
Explanation:
Apache Spark supports multiple programming languages for developing distributed applications:
Java (Option A): Supported through Spark's JVM-based APIs.
Scala (Option B): Spark is natively written in Scala, and Scala APIs provide full functionality.
Python (Option C): Supported via PySpark, enabling Python developers to leverage Spark.
Additionally, Spark also supports R and SQL-like queries, making it versatile for data scientists and engineers.
Thus, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools: Spark APIs for Java, Scala, Python, and R.
NEW QUESTION # 34
Maximum Likelihood Estimation (MLE) is a way to frame:
- A. Small class of problems in HDFS
- B. Both A and C
- C. Small class of problems in Data Science
- D. Large class of problems in HDFS
- E. Large class of problems in Data Science
Answer: E
Explanation:
Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a model by maximizing the likelihood function - i.e., finding the parameters that make the observed data most probable.
Option A: Correct. MLE provides a framework for a large class of problems in data science, including regression, classification, generative models, and probabilistic inference.
Option B: Incorrect - it applies to many problems, not just a small subset.
Option C & D: Incorrect. HDFS (Hadoop Distributed File System) is a storage technology, unrelated to MLE.
Option E: Incorrect because C is invalid.
Thus, the correct answer is Option A (Large class of problems in Data Science).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Foundations: Maximum Likelihood Estimation and Inference in Data Science.
NEW QUESTION # 35
Which of the following is a trend analysis component of time series decomposition?
- A. All of the above
- B. Irregular
- C. Cyclical
- D. Seasonal
- E. Both A and B
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 # 36
Self-driving car is an example of:
- A. All of the above
- B. Unsupervised learning
- C. Reinforcement learning
- D. Supervised learning
Answer: C
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 # 37
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