SDS Exam Dumps - DASCA Data Scientist Questions and Answers

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).

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).

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).

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).

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.

A Bernoulli random variable is the simplest form of discrete random variable.

It can take only two values:

1 with probability p (success).

0 with probability (1 – p) (failure).

Since the outcomes are finite (binary), it is a discrete random variable.

Option B (Continuous): Incorrect, as continuous variables take values from an interval (e.g., real numbers).

Option C and D: Incorrect, as Bernoulli is always discrete, never continuous.

Thus, the correct answer is Option A (Discrete random variable).

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.

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).

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).

Self-driving cars (autonomous vehicles) are an application of Reinforcement Learning (RL) in machine learning:

In RL, an agent (car) interacts with an environment (roads, obstacles, traffic) and learns to maximize rewards (e.g., safe driving, efficient navigation).

The system improves performance through trial-and-error learning, guided by reward signals such as staying in a lane or avoiding collisions.

Supervised learning (A): Used in some supporting tasks like image recognition (e.g., identifying stop signs), but not the core paradigm for self-driving.

Unsupervised learning (B): Useful for clustering sensor data, but again not the main paradigm.

Reinforcement learning (C): Correct, since self-driving fundamentally depends on RL decision-making.

Thus, the correct answer is Option C (Reinforcement Learning).