SDS Senior Data Scientist Questions and Answers

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).

Optical Character Recognition (OCR) is the process of automatically recognizing and converting different types of documents — such as scanned paper documents, PDFs, or images — into editable and searchable text.

OCR systems use Machine Learning (ML) and Computer Vision techniques to detect and classify patterns of characters in images.

Algorithms like Convolutional Neural Networks (CNNs) are commonly used for image-based OCR.

While OCR may indirectly contribute to data mining or big data workflows, the core application is based on machine learning, where models are trained to classify and recognize text patterns.

Thus, OCR is primarily a Machine Learning application, making Option B correct.

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).

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).

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.

HDFS (Hadoop Distributed File System) provides quota management to control and monitor resource usage across directories:

Name Quotas (Option A): Limits the number of files and directories that can be created in a given HDFS directory. Helps prevent excessive metadata growth.

Space Quotas (Option B): Limits the total disk space consumed by files within a directory. Helps in capacity planning and avoiding storage overuse.

Since HDFS supports both types, the correct answer is Option C (Both A and B).

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.

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).

Functional programming (FP) is a paradigm widely adopted in data science and big data tools (e.g., Spark with Scala/Python). Its useful features include:

Option A (Higher-Order Functions): Functions can take other functions as arguments or return them, enabling powerful abstractions like map(), reduce(), and filter().

Option B (Immutable Data): Ensures reliability and thread-safety, crucial for distributed computing. Once created, data structures cannot be modified, preventing side effects.

Option C (Lazy Evaluation): Computations are delayed until results are needed, improving performance in large-scale data operations.

Since FP leverages all three features, the correct answer is Option D (All of the above).

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.

A Box Plot (also called Whisker Plot) is a visualization tool used to summarize data distribution using five-number summary:

Minimum,

First quartile (Q1),

Median (Q2),

Third quartile (Q3),

Maximum.

It also highlights outliers explicitly.

Option A (Box Plots): Correct.

Option B (Pie Charts): Show proportions, not distribution.

Option C (Histogram): Shows frequency distribution but not quartiles/median.

Option D (Scatter Chart): Used for relationships between two variables, not summary statistics.

Option E (Bar Charts): Compare categories, not statistical spread.

Thus, the correct answer is Option A (Box Plots).

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).

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).

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).

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.

Agile methodology is widely adopted in data science projects because these projects often involve uncertain goals, exploratory analysis, and changing requirements. Agile thrives in environments where iteration, collaboration, and adaptability are necessary.

Option A: True for Agile. If the final product is unclear (common in data science), Agile works well because it allows incremental discovery and iterative prototyping.

Option B: True for Agile. Agile frameworks (Scrum, Kanban) emphasize flexibility, which means the scope can evolve as stakeholders learn more from data and models.

Option C: True for Agile. Agile welcomes continuous changes through iterative sprints and feedback loops. This adaptability is crucial in machine learning model development where data insights often reshape project direction.

Since all three situations are valid for Agile, the correct answer to “Which is NOT correct?” is None of the above (Option D).

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).

Cluster management tools help in orchestrating and monitoring large-scale distributed computing environments.

Zettaset Orchestrator (A): Commercial tool for Hadoop cluster management.

Apache Mesos (B): A cluster manager that abstracts CPU, memory, and storage to enable fault-tolerant distributed systems.

Apache Ambari (C): An open-source tool for provisioning, managing, and monitoring Hadoop clusters.

Apache Hadoop (D): Not a cluster management tool. Hadoop is a framework for distributed storage and processing (HDFS + MapReduce), not a management tool.

Thus, the correct answer is Option D (Apache Hadoop).

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).

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).

Apache Spark supports multiple programming languages for developing distributed applications:

Java (Option A): Supported through Spark’s JVM-based APIs.

Scala (Option B): Spark is natively written in Scala, and Scala APIs provide full functionality.

Python (Option C): Supported via PySpark, enabling Python developers to leverage Spark.

Additionally, Spark also supports R and SQL-like queries, making it versatile for data scientists and engineers.

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