DASCA SDS認定試験問題集には87練習テスト問題があります
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質問 # 13
Which of the following is a "thinking like a data scientist" decomposition process?
- A. Business Stakeholder
- B. Business Initiative
- C. Both B and C
- D. Strategic Nouns
- E. All of the above
正解：E
解説：
The "Thinking Like a Data Scientist" (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:
Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).
Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.
Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.
Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Science Fundamentals: Thinking Like a Data Scientist Process.
質問 # 14
Which of these are open-source column-oriented databases?
- A. Accumulo
- B. Both A and B
- C. Cassandra
- D. HBase
- E. All of the above
正解：E
解説：
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.
質問 # 15
Designing an algorithm to play chess is usually an example of which type of machine learning?
- A. Clustering
- B. Pattern density
- C. Supervised learning
- D. Reinforcement learning
正解：D
解説：
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.
質問 # 16
ARIMA model is:
- A. Autoresponsive moving average
- B. Autointeractive moving average
- C. All of the above
- D. Autoregressive moving average
- E. Autoreactive moving average
正解：D
解説：
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).
質問 # 17
In unsupervised learning, learning takes place by based on these deductions in input data and developing patterns:
- A. Detecting irregularities
- B. Detecting regularities
- C. Both A and B
- D. None of the above
正解：C
解説：
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.
質問 # 18
Which of the following is NOT used to implement Agile?
- A. Extreme Programming (XP)
- B. Six Sigma
- C. Scrum
- D. Kanban
正解：B
解説：
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.
質問 # 19
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. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
- C. Both B and C
- D. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
- E. All of the above
正解：E
解説：
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.
質問 # 20
Spark should be used when:
- A. Data is not massive
- B. Data is massive
- C. Both A and B
- D. None of the above
正解：B
解説：
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.
質問 # 21
Which of the following is an example of NLP?
- A. Finding which text documents are about similar topics
- B. Using Twitter to assess public sentiment
- C. Flagging e-mails as spam
- D. Both A and C
- E. All of the above
正解：E
解説：
Natural Language Processing (NLP) is the field of machine learning that deals with human language understanding and generation. Applications include:
Option A (Spam detection): NLP techniques classify emails based on text patterns and context.
Option B (Sentiment analysis on Twitter): NLP models analyze textual data to extract emotions, opinions, and trends.
Option C (Topic modeling): NLP clustering and probabilistic models (e.g., LDA) classify documents by semantic similarity.
Since all are valid NLP applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: NLP in Real- World Use Cases.
質問 # 22
Tar is an example of:
- A. CSV file format
- B. ARV file format
- C. Archive file format
- D. Text file format
- E. None of the above
正解：C
解説：
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.
質問 # 23
Which of the following is correct?
- A. Both A and B
- B. DataFrame is similar to SQL tables or R data frames
- C. The central object in Pandas is called a DataFrame
- D. A data frame is a table with rows and columns
- E. All of the above
正解：E
解説：
Pandas is one of the most widely used Python libraries for data analysis and manipulation. Its central object is the DataFrame.
Option A: Correct. DataFrame is the core data structure in Pandas.
Option B: Correct. DataFrame resembles SQL tables and R data frames, supporting row/column indexing, joins, and grouping.
Option C: Correct. A DataFrame is essentially a 2D labeled table consisting of rows and columns.
Option D: Correct, but not fully inclusive.
Option E: Correct, since all of A, B, and C are true.
Thus, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Pandas Data Structures.
質問 # 24
Exploratory analytic algorithms help the Data Science team to better:
- A. Understand the data content
- B. Understand patterns in the data
- C. Both A and B
- D. Gain a high-level understanding of relationships
- E. All of the above
正解：E
解説：
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.
質問 # 25
Which of the following is correct for Markov chain?
- A. A Markov chain is a sequence of fixed variables X1, X2
- B. A Markov chain is a sequence of random variables X1, X2
- C. Both A and B
- D. Both B and C
- E. A Markov chain is the state of a system at sequential points in time
正解：D
解説：
A Markov chain is a stochastic process describing a sequence of possible events, where the probability of each event depends only on the state attained in the previous step (the Markov property).
Option A: Incorrect. The variables are random, not fixed.
Option B: Correct. Markov chains represent the state of a system at sequential time points.
Option C: Correct. A Markov chain is indeed a sequence of random variables {X1, X2, ...} that satisfy the Markov property.
Option D: Incorrect, since A is wrong.
Option E: Correct, because both B and C are valid.
Thus, the correct answer is Option E (Both B and C).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Probabilistic Models: Markov Chains.
質問 # 26
Which of the following is NOT a process of Use Case?
- A. Capture the decisions that the key business stakeholders need to make in order to support the organization's key business initiatives
- B. Brainstorm the questions that the key stakeholders need to answer to facilitate making the decisions
- C. Understand your organization's key business initiatives or business challenge
- D. Brainstorm the outcomes that the key stakeholders need to answer to facilitate making the decisions
- E. Identify your key business stakeholders
正解：D
解説：
Use Case Development in data science projects involves identifying business needs and mapping analytics to business decisions. The standard steps include:
Option A: Understanding the key initiatives or challenges.
Option B: Identifying the key stakeholders.
Option C: Capturing the decisions stakeholders must make.
Option E: Brainstorming the questions stakeholders need answered to support decisions.
However:
Option D (Brainstorm the outcomes stakeholders need to answer): Incorrect phrasing. It is not "outcomes" that are brainstormed but questions and decisions.
Thus, the correct answer is Option D.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Use Case Development Process.
質問 # 27
Which of the following is a DevOps Practice?
- A. Continuous delivery
- B. Continuous integration
- C. Continuous build
- D. All of the above
正解：D
解説：
DevOps is a collaborative practice that integrates software development (Dev) and IT operations (Ops) to shorten development cycles and deliver applications reliably. Common DevOps practices include:
Continuous Build (Option A): Automating compilation and packaging of source code to ensure consistent builds.
Continuous Integration (Option B): Developers frequently merge code into a shared repository, which is automatically tested to catch integration issues early.
Continuous Delivery (Option C): Automating software release pipelines so applications can be deployed to production quickly and reliably.
Since all of these are essential DevOps practices, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: DevOps Practices in Data Science Projects.
質問 # 28
Which of the following is NOT an example of graphical model?
- A. Flow charts
- B. Road maps
- C. Electrical circuits
- D. Computer networks
- E. Geographical networks
正解：A
解説：
Graphical models represent relationships between objects using nodes (entities) and edges (relationships).
Examples include:
Road maps (Option A): Nodes = intersections, Edges = roads.
Electrical circuits (Option B): Nodes = components, Edges = connections.
Computer networks (Option C): Nodes = devices, Edges = connections.
Geographical networks (Option D): Nodes = locations, Edges = transport or connectivity.
However:
Flow charts (Option E): These represent process flows, not structural networks of entities and relationships.
They are procedural diagrams, not graphical models in the statistical/graph-theory sense.
Thus, the correct answer is Option E (Flow charts).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graphical Models and Graph Analysis.
質問 # 29
The Big Data Vision Workshop process is ideal for organizations who:
- A. Have a desire to leverage Big Data to transform their business but do not know where and how to start
- B. Both A and B
- C. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
- D. Have a wealth of data that they do not know how to monetize
- E. All of the above
正解：E
解説：
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.
質問 # 30
Data wrangling is the process of getting the data from:
- A. Its raw format into something suitable for more conventional analytics
- B. Its modified meaning format into something suitable for more conventional analytics
- C. Both A and B
- D. None of the above
正解：A
解説：
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.
質問 # 31
The Big Data Vision Workshop process is ideal for organizations who:
- A. Have a desire to leverage Big Data to transform their business but do not know where and how to start
- B. Both A and B
- C. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
- D. Have a wealth of data that they do not know how to monetize
- E. All of the above
正解：E
解説：
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.
質問 # 32
Which of the following is the common evolutionary algorithm of neural networks?
- A. Evolution strategy
- B. Genetic algorithm
- C. Genetic programming
- D. All of the above
正解：D
解説：
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.
質問 # 33
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