Free and Premium DASCA SDS Dumps 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.

"By" analysis is one of the foundational approaches recommended in the DASCA Data Scientist Knowledge Framework for structuring problem-solving in data science. The purpose of "By" analysis is to enable data scientists and business stakeholders to think beyond obvious data correlations and uncover deeper drivers of business outcomes.

At its core, the technique reinforces the discipline ofthinking like a data scientist(Option A). This involves reframing business questions into analytical structures and asking “What drives this metricbywhich factors?” For example, customer churn might be analyzedbydemographics, purchase behavior, or service usage. This structured mindset is critical for ensuring scientific rigor in business problem analysis.

In addition, "By" analysis emphasizes collaboration betweenSubject Matter Experts (SMEs)andData Science teams(Option B). SMEs bring contextual domain knowledge, while data scientists bring analytical and statistical expertise. Together, they brainstorm possible explanatory variables or metrics that could become strong predictors of business performance.

Furthermore, the process provides acollaborative bridgebetween business and technical stakeholders (Option C). It ensures that the exploration of data is not isolated in silos but is grounded in both domain insights and advanced analytical methods. This alignment is crucial for building models that are not only technically sound but also relevant and actionable in real-world business contexts.

Since Options A, B, and C are correct and complementary, the best choice isOption E: All of the above.

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).

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).

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.

Apache Spark is a distributed computing framework designed as an improvement over Hadoop’s MapReduce. According to the official Apache Spark documentation:

Spark can run workloads up to 100x faster in memory.

Spark can run workloads up to 10x faster on disk.

This performance gain comes from Spark’s use of in-memory computation, DAG execution engine, and optimized query execution, compared to the slower, disk-heavy Hadoop MapReduce framework.

Thus, the correct statement is Option A.

TAR (Tape Archive) is a widely used archive file format in Unix/Linux environments. It is used to combine multiple files into a single archive file (with extension .tar).

Option A: Correct. TAR is specifically designed for archiving.

Option B (CSV): Incorrect. CSV (Comma-Separated Values) is a tabular text data format.

Option C (ARV): Incorrect — no such format.

Option D (Text): Incorrect. Though TAR may contain text files, the TAR format itself is not plain text but an archive format.

Option E: Incorrect since Option A is valid.

Thus, TAR is an Archive file format.

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).

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).

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).

Graphical models represent relationships between objects using nodes (entities) and edges (relationships). Examples include:

Road maps (Option A): Nodes = intersections, Edges = roads.

Electrical circuits (Option B): Nodes = components, Edges = connections.

Computer networks (Option C): Nodes = devices, Edges = connections.

Geographical networks (Option D): Nodes = locations, Edges = transport or connectivity.

However:

Flow charts (Option E): These represent process flows, not structural networks of entities and relationships. They are procedural diagrams, not graphical models in the statistical/graph-theory sense.

Thus, the correct answer is Option E (Flow charts).

Apache Spark supports multiple programming languages for developing distributed applications:

Java (Option A): Supported through Spark’s JVM-based APIs.

Scala (Option B): Spark is natively written in Scala, and Scala APIs provide full functionality.

Python (Option C): Supported via PySpark, enabling Python developers to leverage Spark.

Additionally, Spark also supports R and SQL-like queries, making it versatile for data scientists and engineers.

Thus, the correct answer is Option D (All of the above).

Python supports multiple libraries for scientific computing and data analysis, but the primary machine learning library is:

Scikit-learn (Option B): Provides a wide range of machine learning algorithms for classification, regression, clustering, model evaluation, and preprocessing. It is the core ML library in Python.

NumPy (Option A): Provides numerical computing and array operations, essential for ML but not a machine learning library itself.

Matplotlib (Option C): Used for data visualization.

SciPy (Option D): Supports scientific computing and numerical methods, not focused on ML models.

Therefore, the correct answer is Option B (Scikit-learn).

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).

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).

A daily stand-up meeting (also called a daily Scrum) is a short meeting (usually 15 minutes) that Agile teams hold to synchronize progress. Its agenda is structured around three key questions:

What was accomplished yesterday? (Progress review).

What is planned for today? (Work alignment).

What impediments or roadblocks exist? (Barriers identification).

This process enhances transparency, communication, and accountability, ensuring the team can quickly address obstacles and stay aligned with sprint goals.

Option A: Correct — yesterday’s work is discussed.

Option B: Correct — today’s planned tasks are outlined.

Option C: Correct — roadblocks are highlighted.

Option D: Incomplete since it misses C.

Option E: Correct — covers all agenda items.

Thus, the correct answer is Option E (All of the above).

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).

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).

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).

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).

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).

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).

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.

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).

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).