Pass the DASCA Data Scientist SDS Questions and answers with ExamsMirror

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).

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).

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.

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).

Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.

Typical components of IPO include:

Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.

Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.

Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.

However:

Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.

Therefore, the correct answer is Option B (Business Metamorphosis).

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).

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).

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).

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.