Practice Free SDS Senior Data Scientist Exam Questions Answers With Explanation

Use Case Development in data science projects involves identifying business needs and mapping analytics to business decisions. The standard steps include:

Option A: Understanding the key initiatives or challenges.

Option B: Identifying the key stakeholders.

Option C: Capturing the decisions stakeholders must make.

Option E: Brainstorming the questions stakeholders need answered to support decisions.

However:

Option D (Brainstorm the outcomes stakeholders need to answer): Incorrect phrasing. It is not “outcomes” that are brainstormed but questions and decisions.

Thus, the correct answer is Option D.

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.

A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.

Option A: Incorrect. Burn down charts don’t measure team “energy” or motivation levels.

Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.

Option C: Incorrect. Hours worked after dark is irrelevant.

Option D: Incorrect. Budget reduction is not tracked in burn down charts.

Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.

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).

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).

"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.

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).

Scribe is a server developed by Facebook for aggregating log data streamed in real time from many servers.

Its primary aim was reliability and simplicity in collecting logs at scale.

Unlike other systems, Scribe was designed to avoid complex or extensive protocols and to ensure lightweight log transfer.

It also avoids unnecessary high disk usage, relying instead on efficient pipelines.

Thus, the correct answer is Option D (None of the above).

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).

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).

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).

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).

Regression is a supervised learning technique where a model estimates the relationship between input features (independent variables) and an output (dependent variable).

Option A: Correct. The learning process involves optimizing model parameters (e.g., coefficients in linear regression) to minimize approximation error. Common loss functions include Mean Squared Error (MSE) or Mean Absolute Error (MAE).

Option B: Correct. Minimizing error enables the model to produce the closest possible outcomes to the actual observed values, ensuring accurate predictions.

Option C: Correct, since both A and B are true.

Option D: Incorrect.

Thus, regression optimization in machine learning aims to minimize approximation error and generate closest possible outcomes, making Option C the correct answer.

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.

Column-oriented databases store data by columns rather than by rows, enabling efficient queries over large datasets, especially in analytical workloads.

Cassandra (Option A): An open-source, highly scalable, distributed column-oriented NoSQL database.

HBase (Option B): An open-source, Hadoop-based, column-family NoSQL database modeled after Google BigTable.

Accumulo (Option C): An open-source, secure, sorted, distributed key/value store built on top of HDFS and based on Google BigTable.

Since all three (A, B, and C) are open-source column-oriented databases, 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).

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.

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.

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 “Thinking Like a Data Scientist” (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:

Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).

Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.

Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.

Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).