SDS Senior Data Scientist Questions and Answers

Service Level Agreements (SLAs) define performance commitments between service providers (like ISPs) and customers. Key SLA metrics include:

MTBF (Option A): Measures reliability by defining the expected average time between service failures.

MTTR (Option B): Measures availability by defining how quickly service can be restored after a failure.

TAT (Option C): Measures responsiveness in resolving customer requests or incidents.

All three are standard SLA performance specifications. Hence, the correct answer is Option D (All of the above).

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).

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.

A null hypothesis (H₀) 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 (H₁), not null.

Thus, the correct answer is Option A.

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).

Python supports multiple libraries for scientific computing and data analysis, but the primary machine learning library is:

Scikit-learn (Option B): Provides a wide range of machine learning algorithms for classification, regression, clustering, model evaluation, and preprocessing. It is the core ML library in Python.

NumPy (Option A): Provides numerical computing and array operations, essential for ML but not a machine learning library itself.

Matplotlib (Option C): Used for data visualization.

SciPy (Option D): Supports scientific computing and numerical methods, not focused on ML models.

Therefore, the correct answer is Option B (Scikit-learn).

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).

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).

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).

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).

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).

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.

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.

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).

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.

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.

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).

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).

Apache Avro is a widely used framework within the Hadoop ecosystem for data serialization and data exchange.

Option A (Correct): Avro is a compact, fast, binary data serialization format. It allows efficient storage and exchange of structured data.

Option B (Correct): Avro supports Remote Procedure Call (RPC). It provides a framework for RPC communication, making it easier for distributed applications to exchange data across systems.

Option C: Correct, since both statements are true.

Option D: Incorrect because Avro is indeed both a serialization framework and RPC-based.

In data engineering workflows, Avro is valuable because it is schema-based (defined using JSON), highly interoperable, and ensures compatibility across different programming languages. This makes it essential in big data pipelines, Kafka messaging, and Hadoop ecosystem tools.

Thus, the correct answer is Option C (Both A and B).

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).

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).