DASCA SDS Senior Data Scientist Exam Practice Test

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.

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).

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).

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).

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).

The DevOps movement evolved as a natural extension of the Agile methodology.

Agile (Option A): Agile emphasizes iterative development, collaboration, and flexibility. While Agile improved software development speed, it created challenges in integrating development with IT operations. DevOps emerged to address this by bringing operations into the Agile cycle — enabling continuous integration, delivery, and deployment.

Waterfall (Option B): Incorrect. Waterfall is a rigid, sequential methodology, fundamentally opposite to the DevOps philosophy.

Promise-based algorithms (Option C): Not a methodology — irrelevant here.

Test-driven development and model-driven development (Option D): While these practices support DevOps, they are not the origin of the movement.

Thus, the DevOps movement is an outgrowth of Agile methodology.

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.

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).

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).

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).

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).

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).

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).

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).

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).

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.

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 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).

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).

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).

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).

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).

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.

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.