SDS Senior Data Scientist Questions and Answers

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.

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.

A daily stand-up meeting (also called a daily Scrum) is a short meeting (usually 15 minutes) that Agile teams hold to synchronize progress. Its agenda is structured around three key questions:

What was accomplished yesterday? (Progress review).

What is planned for today? (Work alignment).

What impediments or roadblocks exist? (Barriers identification).

This process enhances transparency, communication, and accountability, ensuring the team can quickly address obstacles and stay aligned with sprint goals.

Option A: Correct — yesterday’s work is discussed.

Option B: Correct — today’s planned tasks are outlined.

Option C: Correct — roadblocks are highlighted.

Option D: Incomplete since it misses C.

Option E: Correct — covers all agenda items.

Thus, the correct answer is Option E (All of the above).

A Bernoulli random variable is the simplest form of discrete random variable.

It can take only two values:

1 with probability p (success).

0 with probability (1 – p) (failure).

Since the outcomes are finite (binary), it is a discrete random variable.

Option B (Continuous): Incorrect, as continuous variables take values from an interval (e.g., real numbers).

Option C and D: Incorrect, as Bernoulli is always discrete, never continuous.

Thus, the correct answer is Option A (Discrete random variable).

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).

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).

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).

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).

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).

"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.

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).

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).

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).

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).

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.

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.

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).

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).

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.

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.

Structured data refers to highly organized data stored in predefined formats such as rows and columns.

Relational databases (Option A): Structured.

Data tables (Option B): Structured.

CSV files (Option C): Structured because they are tabular with rows and columns.

XML (Option D): Not structured but semi-structured because it stores hierarchical information with tags and attributes.

Thus, the correct answer is Option D (XML).

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).