SDS Questions and Answers

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).

The Chief Data Monetization Officer (CDMO) is a C-level executive role emerging in data-driven organizations. Their focus is on turning data into business value and revenue growth.

Statement i: Correct. The CDMO is responsible for value realization from data and analytics investments.

Statement ii: Correct. The CDMO owns decisions around investment prioritization for data initiatives.

Statement iii: Correct. Since monetization is directly linked to business performance, the CDMO must be accountable for revenue and margin impact.

Hence, all three statements are correct, making the right answer Option C (All of the above).

In data pipelines and process orchestration, a workflow is represented as a Directed Acyclic Graph (DAG):

Directed: Each edge has a direction, representing task dependencies.

Acyclic: No cycles exist; tasks must follow a sequence without looping back.

Graph: Represents tasks as nodes and dependencies as edges.

This structure is common in tools like Apache Airflow, Spark DAGs, and Hadoop MapReduce job schedulers.

Option A & B: Incorrect, as workflows cannot have cycles (would cause infinite loops).

Option D: Incorrect, because workflows are directed, not indirected.

Thus, the correct answer is Option C (Directed Acyclic Graph).

The Big Data Business Model Maturity Index (BDBMMI) defines phases organizations pass through in leveraging data strategically:

Business Monitoring (A): Tracking metrics and reporting.

Business Insights (not listed in options but part of the framework).

Business Optimization (B): Using analytics to improve efficiency.

Data Monetization (D): Creating new revenue streams with data.

Business Metamorphosis (E): Transforming the business model through data.

Business Strategy (Option C): While strategy is essential, it is not one of the defined phases of BDBMMI.

Thus, the correct answer is Option C (Business Strategy).

Machine learning (ML) can be broadly divided into three main paradigms:

Supervised Learning (Option A):

Data includes labeled outputs (e.g., classification, regression).

Goal: Learn a mapping from input to output.

Unsupervised Learning (Option B):

Data has no labels.

Goal: Discover hidden patterns (e.g., clustering, dimensionality reduction).

Reinforcement Learning (Option C):

Agent interacts with an environment and learns by maximizing cumulative rewards through trial and error.

Used in robotics, game AI, and autonomous systems.

Since all three categories are valid, the correct answer is Option D (All of the above).

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).

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).

"By" analysis is one of the foundational approaches recommended in the DASCA Data Scientist Knowledge Framework for structuring problem-solving in data science. The purpose of "By" analysis is to enable data scientists and business stakeholders to think beyond obvious data correlations and uncover deeper drivers of business outcomes.

At its core, the technique reinforces the discipline ofthinking like a data scientist(Option A). This involves reframing business questions into analytical structures and asking “What drives this metricbywhich factors?” For example, customer churn might be analyzedbydemographics, purchase behavior, or service usage. This structured mindset is critical for ensuring scientific rigor in business problem analysis.

In addition, "By" analysis emphasizes collaboration betweenSubject Matter Experts (SMEs)andData Science teams(Option B). SMEs bring contextual domain knowledge, while data scientists bring analytical and statistical expertise. Together, they brainstorm possible explanatory variables or metrics that could become strong predictors of business performance.

Furthermore, the process provides acollaborative bridgebetween business and technical stakeholders (Option C). It ensures that the exploration of data is not isolated in silos but is grounded in both domain insights and advanced analytical methods. This alignment is crucial for building models that are not only technically sound but also relevant and actionable in real-world business contexts.

Since Options A, B, and C are correct and complementary, the best choice isOption E: All of the above.

LaTeX is a high-quality typesetting system widely used in academia, particularly in scientific publishing.

Statement i: Correct. LaTeX is widely used to prepare manuscripts for scientific journals, theses, and technical reports.

Statement ii: Correct. LaTeX is a markup language (similar to HTML in concept) that compiles into formatted PDFs/documents.

Statement iii: Correct. LaTeX is a standard for publishing scientific papers due to its ability to handle complex mathematical equations, references, and formatting.

Thus, all three statements are true → Option B (i, ii, iii).

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.

Microservices architecture is a software design approach where applications are developed as a collection of small, loosely coupled, and independently deployable services.

Option A: Correct. Each microservice runs independently and has its own lifecycle, allowing scalability and modularity.

Option B: Correct. Each service can be treated as an independent project with its own repository, build pipeline, and deployment.

Option C: Correct. Microservices can be developed in different programming languages or frameworks, chosen based on the best fit for each specific requirement.

Therefore, the correct answer is Option D (All of the above).

Grid computing is a distributed computing model where resources (CPU, memory, storage) are pooled across multiple systems to solve large-scale problems.

Option A (Divide): Middleware helps allocate or divide resources dynamically to different tasks.

Option B (Combine): Middleware integrates diverse resources into a unified system, making them accessible for parallel computing.

Option C: Correct — middleware is the “glue” that enables both combining and dividing resources seamlessly across distributed nodes.

Option D: Incorrect.

Thus, grid computing middleware both combines and divides resources, making Option C correct.

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).

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).

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).

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).

In Python, the ElementTree module (part of the standard library xml.etree.ElementTree) provides a simple and efficient API for parsing and creating XML data.

The main feature of ElementTree is its ability to provide direct access to the parse tree of an XML document.

This allows developers to:

Parse XML into an in-memory tree structure.

Traverse, search, modify, and extract information from XML elements.

Write back changes into XML files.

Options B, C, and D (Delete, Copy, Insert tree) are not standard terminology in XML handling with ElementTree. While you can delete, insert, or copy elements, the module itself primarily gives parse tree access.

Thus, the correct answer is Option A (Parse tree of the XML).

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.

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).

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).