[2025年09月01日]DASCA SDSリアル試験問題と解答を無料で提供いたします [Q51-Q76]

[2025年09月01日]DASCA SDSリアル試験問題と解答を無料で提供いたします

合格できるDASCA SDS試験情報と無料練習テスト問題

質問 # 51
Business Intelligence (BI) is:

A. Both A and B
B. BI focuses on "What happened?"
C. BI focuses on reporting on the future state of the business
D. BI focuses on descriptive analytics
E. Both B and C

正解：A

解説：
Business Intelligence (BI) is primarily focused on descriptive analytics and reporting - understanding historical and current business performance.
Option A (Descriptive analytics): Correct. BI uses dashboards, reports, and OLAP tools to summarize what has occurred in the past.
Option B ("What happened?"): Correct. BI answers retrospective questions by analyzing transactional and operational data.
Option C (Future state): Incorrect. Predicting future business outcomes falls under predictive analytics or advanced analytics, not BI.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization & BI: Descriptive Analytics and Reporting.

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

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

正解：B

解説：
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.

質問 # 53
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 50 times faster in memory and 5 times faster on disk in comparison to the traditional Hadoop MapReduce
D. 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

正解：B

解説：
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.

質問 # 54
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. Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
E. Both B and C

正解：A

解説：
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.

質問 # 55
The spokes of the "Hub and Spoke" analytics architecture are the analytic use cases or applications that help the organization to optimize:

A. All of the above
B. Deliver a more compelling customer experience
C. Both A and B
D. Uncover new monetization opportunities
E. Key business processes

正解：A

解説：
In the Hub and Spoke analytics architecture:
The hub is the central data platform (data lake, warehouse, or unified data hub).
The spokes are the analytic use cases or applications that leverage this data to create business value.
These spokes typically help the organization:
Optimize key business processes (Option A).
Deliver improved customer experiences (Option B).
Uncover monetization opportunities (Option C).
Since all three are valid, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Hub-and-Spoke Analytics.

質問 # 56
Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized

A. None of the above
B. i, ii
C. ii, iii
D. i, iii

正解：B

解説：
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.

質問 # 57
Which of the following is FALSE for Social Network Analysis (SNA)?

A. None of the above
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. Social Network Analysis (SNA) is an example of graph analysis

正解：C

解説：
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.

質問 # 58
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?

A. Normal Curve Equivalent (NCE)
B. Medium Curve Equivalent (MCE)
C. None of the above
D. Trend analysis
E. High Curve Equivalent (HCE)

正解：A

解説：
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.

質問 # 59
What is Scrum?

A. None of the above
B. Agile is a subset of Scrum
C. Scrum is a subset of Agile
D. Scrum and Agile are the same

正解：C

解説：
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.

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

A. None of the above
B. Its modified meaning format into something suitable for more conventional analytics
C. Both A and B
D. Its raw format into something suitable for more conventional analytics

正解：D

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 # 61
Which of the following is NOT an example of the applications of neural networks?

A. Character recognition
B. None of the above
C. Image compression
D. Traveling salesman's problem
E. Stock market prediction

正解：D

解説：
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.

質問 # 62
Which of the following is an example of graphical model?

A. Markov Random Fields
B. Both A and B
C. Both A and C
D. Bayesian Networks
E. Geographical Networks

正解：B

解説：
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).

質問 # 63
Which of the following is correct about customer lifetime value (CLTV)?
i. Most organizations determine the current customer lifetime value (CLTV) based on historic sales over past
12 to 18 months
ii. The goal of the CLTV score is to help marketing and store personnel to determine the "value" of a customer

A. Both i and ii
B. Only i
C. Only ii

正解：A

解説：
Customer Lifetime Value (CLTV) is a predictive metric estimating the total revenue a business can reasonably expect from a customer during their entire relationship.
Statement i: Correct. Many organizations calculate CLTV using historic transactional data, often looking at sales records over the past 12-18 months to establish baselines.
Statement ii: Correct. The primary purpose of CLTV is to help marketing, sales, and retail teams understand customer value, enabling them to allocate budgets effectively for retention, promotions, and personalized marketing.
Thus, both statements are correct # Option C (Both i and ii).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: CLTV Metrics and Marketing Analytics.

質問 # 64
JSON takes hierarchical data structures and serializes them into:

A. Both A and B
B. None of the above
C. Plain string format
D. Any desired format
E. Plain text format

正解：A

解説：
JSON (JavaScript Object Notation) is a lightweight data-interchange format widely used for storing and exchanging structured or semi-structured data. JSON allows hierarchical (tree-like) structures, such as nested objects and arrays, to be serialized into a textual representation.
Option A (Plain text format): Correct. JSON files are stored as plain text, making them human-readable and language-independent.
Option B (Plain string format): Correct. JSON objects are transmitted as strings across networks (e.g., via APIs, RESTful services).
Option C: Incorrect. JSON does not serialize into "any format," but specifically into text/string-based formats.
Option D: Correct. Since JSON is both plain text and transmitted as string format, the right answer is both A and B.
Option E: Incorrect.
Thus, JSON serializes hierarchical data into plain text and string formats.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Tools: Data Serialization Formats (JSON, XML, Avro).

質問 # 65
Which of the following can be classified as factor analysis in machine learning?

A. Both A and B
B. None of the above
C. Confirmatory factor analysis
D. Exploratory factor analysis

正解：A

解説：
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.

質問 # 66
The DevOps movement is an outgrowth of which of the following software development methodologies?

A. Agile
B. Test-driven development and model-driven development
C. Promise-based algorithms
D. Waterfall

正解：A

解説：
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.

質問 # 67
Which of the following is TRUE for Business Metamorphosis?

A. All of the above
B. The Business Metamorphosis phase helps drive an organization's core business model through the analytic insights gathered as the organization traverses the Big Data Business Model Maturity Index
C. Business Metamorphosis exercise can uncover Big Data requirements around decisions, analytics and data sources that can be leveraged to transform or metamorphose your organization's business model
D. Both A and C
E. The Business Metamorphosis phase is where organizations integrate the insights that they captured about their customers' usage patterns, product performance behaviors, and overall market trends to transform their business models

正解：A

解説：
Business Metamorphosis is the most advanced phase in the Big Data Business Model Maturity Index (BDBMMI), where organizations fundamentally transform their business models through analytics-driven insights.
Option A: Correct. This phase helps organizations identify big data requirements related to decisions, analytics, and sources that drive business transformation.
Option B: Correct. Organizations integrate customer usage patterns, product behaviors, and market trends into their decision-making to redesign or innovate their business model.
Option C: Correct. Business Metamorphosis ensures that the core business model evolves continuously, guided by insights derived across maturity stages.
Since all are correct, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Business Model Maturity Index.

質問 # 68
Which of the following is a trend analysis component of time series decomposition?

A. Irregular
B. Seasonal
C. Cyclical
D. All of the above
E. Both A and B

正解：D

解説：
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.

質問 # 69
Tar is an example of:

A. Archive file format
B. None of the above
C. ARV file format
D. CSV file format
E. Text file format

正解：A

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 # 70
Machine learning can be used in:

A. Fraud detection
B. Pattern and image recognition
C. Web search results
D. All of the above
E. Real-time ads on web pages and mobile devices

正解：D

解説：
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.

質問 # 71
Which of these statements reflects a null hypothesis?

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

正解：C

解説：
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.

質問 # 72
Semi-structured data does NOT include:

A. Scientific data
B. File systems
C. Schema-full data
D. Database system

正解：C

質問 # 73
What is DevOps?

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

正解：C

解説：
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.

質問 # 74
IoT is built on:

A. Cloud Computing
B. Both A and B
C. None of the above
D. Networks of data gathering devices

正解：B

解説：
The Internet of Things (IoT) is an ecosystem of interconnected devices that collect, transmit, and analyze data. IoT relies on two critical foundations:
Option A (Cloud Computing): IoT generates massive amounts of data, and cloud platforms provide scalable storage, analytics, and computing resources for real-time and batch processing.
Option B (Networks of data gathering devices): IoT relies on physical devices - sensors, smart appliances, industrial machines - that collect and transmit data through networks (Wi-Fi, Bluetooth, 5G, LPWAN).
Thus, IoT is fundamentally built on both cloud computing and networks of devices, making Option C correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data & IoT Ecosystem Fundamentals.

質問 # 75
Which of the following is an example of NLP?

A. Using Twitter to assess public sentiment
B. All of the above
C. Both A and C
D. Flagging e-mails as spam
E. Finding which text documents are about similar topics

正解：B

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 # 76
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