DASCA SDS Senior Data Scientist Exam Practice Test

Python’s built-in data containers are widely used in data science and programming:

Lists (Option A): Ordered, mutable sequences.

Tuples (Option B): Ordered, immutable sequences.

Dict (Option D): Dictionaries, key-value data structures implemented using hash tables.

LinkedList (Option C): Python does not provide a native linked list as a built-in container. While linked lists can be implemented manually or through external libraries (e.g., collections.deque for efficient appends/pops), they are not part of Python’s main native containers.

Thus, the correct answer is Option C (LinkedList).

In hypothesis testing, two main types of errors are defined:

Type I Error (Option A): Occurs when the null hypothesis (H₀) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.

Type II Error (Option B): Occurs when the null hypothesis (H₀) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.

Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.

Thus, the “wrong negation of a true null hypothesis” refers to a Type I Error (false positive).

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 correct answer isPyMC(Option B).

PyMC is an open-source Python library widely used forBayesian statistical modelingandprobabilistic machine learning. It provides a robust framework for defining and fitting Bayesian networks to real data usingMarkov Chain Monte Carlo (MCMC)sampling techniques, as well asvariational inferencemethods. This makes it a powerful tool for data scientists who want to work withuncertainty modeling,probabilistic inference, andcausal reasoningin complex datasets.

Let’s clarify the other options to avoid confusion:

Option A: SciLib– There is no standard Python library by this name that is related to Bayesian networks. (It may be confused withSciPyorSciKit-Learn, but those are not specialized for Bayesian inference.)

Option C: MyLib– This is not a recognized Python package in the data science ecosystem.

Option D: MCMC– While Markov Chain Monte Carlo is thetechniqueused in Bayesian estimation, it is not a standalone library. Instead, PyMC implements MCMC as part of its computational framework.

Option E: SCIMC– No such Python library exists; it appears to be a distractor.

PyMC’s primary strength is its ability to let data scientists define models in aprobabilistic programming style, making it easier to represent uncertainties and hidden variables in data. This aligns with DASCA’s emphasis on ensuring data scientists understand bothstatistical foundationsand thetools required to implement them programmatically.

In practice, PyMC is often used in applications such as:

Forecasting(e.g., time series with uncertainty bounds)

Causal inference(estimating hidden relationships in data)

Risk modeling(finance, healthcare, or supply chain domains)

Machine learning with uncertainty quantification

Thus,PyMCis the correct library for fitting Bayesian networks in Python.

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).

Neural networks have been widely applied in various domains:

Option A (Character recognition): Correct application — neural networks are highly effective for OCR (Optical Character Recognition).

Option B (Stock market prediction): Correct application — neural networks are used to model time-series and nonlinear patterns in finance.

Option D (Image compression): Correct application — neural nets (autoencoders) are used for dimensionality reduction and compression.

Option C (Traveling salesman’s problem): NOT a typical neural network application. This is a combinatorial optimization problem usually solved with heuristics, dynamic programming, or optimization algorithms (not standard neural networks).

Thus, the correct answer is Option C (Traveling salesman’s problem).

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).

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.

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.

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 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 Statement of Work (SOW) is a formal document that defines the scope, objectives, deliverables, timeline, and expectations of a project. In data science and IT projects, it ensures:

Clarity of scope (Option A): Everyone understands exactly what work should be done.

Clear priorities (Option B): It defines what is most critical for success.

Realistic expectations (Option C): It aligns stakeholders by setting measurable and achievable goals.

Since all of these are essential purposes of an SOW, the correct answer is Option D (All of the above).

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 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.

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).

Inductive learning techniques in machine learning (such as decision trees, neural networks, or SVMs) follow a systematic sequence of steps for classification:

Training Step (i): A model is built using training data, where the system learns relationships between features and target labels.

Test Step (ii): The trained model is evaluated on unseen test data to measure its performance and generalizability.

Validation Step (iii): Often, a validation set is used to fine-tune model parameters, avoid overfitting, and choose the best model configuration.

Application Step (iv): The final validated model is applied to classify new, real-world data.

Since all four steps (i, ii, iii, iv) are essential to inductive classification, the correct answer is Option D (i, ii, iii, iv).

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).

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.

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).

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).

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).

Business Metamorphosis is the most advanced phase in the Big Data Business Model Maturity Index (BDBMMI), where organizations fundamentally transform their business models through analytics-driven insights.

Option A: Correct. This phase helps organizations identify big data requirements related to decisions, analytics, and sources that drive business transformation.

Option B: Correct. Organizations integrate customer usage patterns, product behaviors, and market trends into their decision-making to redesign or innovate their business model.

Option C: Correct. Business Metamorphosis ensures that the core business model evolves continuously, guided by insights derived across maturity stages.

Since all are correct, the best answer is Option E (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.

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).