Free DASCA SDS Practice Exam with Questions & Answers

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.

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).

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).

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.

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).

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).

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).

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).

Images are broadly categorized based on how they store visual information:

Rasterized images (Option i):

Composed of a grid of pixels (bitmap).

Each pixel has color information.

Examples: JPEG, PNG, BMP.

Best for photos or complex visuals.

Vectorized images (Option ii):

Composed of paths defined by mathematical formulas.

Scalable without quality loss.

Examples: SVG, EPS, AI.

Best for logos, icons, and illustrations.

Sectorized images (Option iii):

Not a standard category in computer graphics.

Thus, image files are categorized into Rasterized and Vectorized, making Option A (i, ii) correct.

Exploratory analytics (often referred to as Exploratory Data Analysis – EDA) is a fundamental step in data science, enabling practitioners to discover initial insights, detect anomalies, and understand the structure of datasets before applying predictive or prescriptive modeling.

Option A (Understand the data content): Correct. EDA techniques (descriptive statistics, summary tables, profiling) reveal missing values, data types, and distributions.

Option B (Gain a high-level understanding of relationships): Correct. Correlation analysis, scatter plots, and cross-tabulations help identify dependencies between variables.

Option C (Understand patterns in the data): Correct. Visualization and clustering methods help discover hidden structures, seasonalities, and outliers.

Since exploratory algorithms contribute to all of these objectives, the correct answer is Option E (All of the above).