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NEW QUESTION # 16
Which of the following is FALSE for Social Network Analysis (SNA)?
- A. Social Network Analysis (SNA) is an example of graph analysis
- B. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
- C. Social Network Analysis (SNA) is an example of trend analysis
- D. SNA is used to investigate social structures and relationships across social networks
- 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 # 17
Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized
- A. i, iii
- B. i, ii
- C. ii, iii
- D. None of the above
Answer: B
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 # 18
Which of the following is TRUE for data lake?
- A. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized
- B. The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
- C. The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive
- D. The data lake can make both of the Business Intelligence and Data Science environments more agile and more productive
- E. None of the above
Answer: A,B,D
Explanation:
But per MCQ single-choice format # answer: A (though ideally A, B, C are correct).
A data lake is a centralized repository designed to store raw, structured, semi-structured, and unstructured data at scale. It provides:
Agility and productivity (Option A): Data lakes support flexible ingestion and faster access, making BI and data science environments more efficient.
Data integration (Option B): They handle multiple types of data, enabling advanced analytics and machine learning use cases.
Data as an asset (Option C): They shift perspective, treating data as a strategic resource, not just a storage cost.
Option D: Incorrect. Data lakes improve agility, not reduce it.
Option E: Incorrect, since multiple true statements exist.
Thus, the correct choice per DASCA context is Option A (with B and C also being true).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering: Data Lakes vs Warehouses.
NEW QUESTION # 19
Designing an algorithm to play chess is usually an example of which type of machine learning?
- A. Supervised learning
- B. Pattern density
- C. Clustering
- D. Reinforcement learning
Answer: D
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 # 20
Which of the following is TRUE about Avro?
- A. Both A and B
- B. Avro is a data serialization framework
- C. Avro is based on Remote Procedure Call (RPC)
- D. None of the above
Answer: A
Explanation:
Apache Avro is a widely used framework within the Hadoop ecosystem for data serialization and data exchange.
Option A (Correct): Avro is a compact, fast, binary data serialization format. It allows efficient storage and exchange of structured data.
Option B (Correct): Avro supports Remote Procedure Call (RPC). It provides a framework for RPC communication, making it easier for distributed applications to exchange data across systems.
Option C: Correct, since both statements are true.
Option D: Incorrect because Avro is indeed both a serialization framework and RPC-based.
In data engineering workflows, Avro is valuable because it is schema-based (defined using JSON), highly interoperable, and ensures compatibility across different programming languages. This makes it essential in big data pipelines, Kafka messaging, and Hadoop ecosystem tools.
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem Tools & Data Serialization Techniques.
NEW QUESTION # 21
Which classification steps are performed in inductive techniques?
i. Training Step
ii. Test Step
iii. Validation Step
iv. Application Step
- A. i, ii, iii, iv
- B. ii, iii
- C. i, ii, iv
- D. i, ii
Answer: A
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 # 22
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
- A. Medium Curve Equivalent (MCE)
- B. High Curve Equivalent (HCE)
- C. Trend analysis
- D. None of the above
- E. Normal Curve Equivalent (NCE)
Answer: E
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 # 23
Which of the following is CORRECT?
i. CDMO should NOT own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
ii. CDMO should own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
iii. CIO should NOT own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
iv. CIO should own the data lake, the data warehouse, or any of the underlying data architecture or technologies.
- A. i, iii
- B. ii, iii
- C. i, iv
- D. ii, iv
Answer: C
Explanation:
The Chief Data Monetization Officer (CDMO) is responsible for driving value extraction from data assets, not owning infrastructure. By contrast, the Chief Information Officer (CIO) traditionally owns technology infrastructure, including data lakes, warehouses, and data architectures.
Statement i: Correct. CDMO should NOT own data infrastructure. Their role is strategic - monetizing insights, not managing systems.
Statement ii: Incorrect. CDMO focuses on monetization, not architecture ownership.
Statement iii: Incorrect. CIO indeed does own these systems.
Statement iv: Correct. CIO owns the data lake, data warehouse, and underlying architectures/technologies.
Thus, the correct combination is i and iv # Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Roles in Data Leadership: CIO vs CDMO.
NEW QUESTION # 24
In regression, the principle of machine learning is used to optimize the parameters to:
- A. Both A and B
- B. Minimize the approximation error
- C. Calculate the closest possible outcomes
- 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 # 25
What is Scrumban?
- A. It is Kanban
- B. It is Scrum
- C. It combines the principles of Scrum and Kanban into a pull-based system
- D. It combines the principles of Scrum and Kanban into a push-based system
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 # 26
Data wrangling is the process of getting the data from:
- A. Both A and B
- B. Its modified meaning format into something suitable for more conventional analytics
- C. Its raw format into something suitable for more conventional analytics
- D. None of the above
Answer: C
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 # 27
Which of the following is NOT a main data container in Python?
- A. Dict
- B. Tuples
- C. LinkedList
- D. Lists
Answer: C
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 # 28
What is TRUE for "rehashing"?
- A. Both A and B
- B. It requires a new hash function, which maps values into a larger range of integers
- C. Allocate a new, larger hash table in memory
- D. Key/value pairs from the original table can be inserted into the new, larger one
- E. All of the above
Answer: E
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 # 29
Which of the following can be classified as factor analysis in machine learning?
- A. Both A and B
- B. Exploratory factor analysis
- C. Confirmatory factor analysis
- D. None of the above
Answer: A
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 # 30
Which of the following is NOT a valid section of Big Data Strategy document?
- A. Business strategy
- B. Key decisions
- C. Key business initiatives
- D. Key business entities
- E. Business decisions
Answer: E
Explanation:
A Big Data Strategy document provides a framework for aligning data initiatives with organizational objectives. It typically includes:
Business strategy (Option A): Ensures that big data initiatives align with overall corporate strategy.
Key decisions (Option B): Identifies the decisions data will help optimize or automate.
Key business initiatives (Option D): Links big data projects with critical organizational initiatives.
Key business entities (Option E): Defines the core entities (customers, products, channels) around which data will be organized.
However:
Business decisions (Option C): This is redundant and not a standard section; "key decisions" covers this aspect already.
Thus, the correct answer is Option C (Business decisions).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Strategy and Business Alignment.
NEW QUESTION # 31
Which of the following is the most important part of Hadoop?
- A. MapReduce Framework
- B. Both A and B
- C. Spark Framework
- D. Hadoop Distributed File System (HDFS)
- E. Both B and C
Answer: B
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 # 32
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