Free and Premium DASCA SDS Dumps Questions Answers

A Tensor is a fundamental data structure in modern machine learning frameworks (e.g., TensorFlow, PyTorch). It is best described as a generalization of vectors and matrices to potentially higher dimensions.

Option A: Correct. Tensors typically store numeric values (commonly floating-point numbers) in structured formats.

Option B: Correct. A tensor can have any number of dimensions (rank). For example:

A scalar is a 0-D tensor.

A vector is a 1-D tensor.

A matrix is a 2-D tensor.

Higher-rank tensors can represent images, videos, or multidimensional datasets.

Option C: Correct. Tensors are explicitly designed to allow linear algebra operations, which are the foundation of deep learning computations (matrix multiplications, dot products, etc.).

Therefore, since all three statements are true, the correct answer is Option E (All of the above).

The Chief Data Monetization Officer (CDMO) is responsible for driving value extraction from data assets, not owning infrastructure. By contrast, the Chief Information Officer (CIO) traditionally owns technology infrastructure, including data lakes, warehouses, and data architectures.

Statement i: Correct. CDMO should NOT own data infrastructure. Their role is strategic — monetizing insights, not managing systems.

Statement ii: Incorrect. CDMO focuses on monetization, not architecture ownership.

Statement iii: Incorrect. CIO indeed does own these systems.

Statement iv: Correct. CIO owns the data lake, data warehouse, and underlying architectures/technologies.

Thus, the correct combination is i and iv → Option C.

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).

Parallel processing architectures are designed to execute multiple instructions or operations simultaneously:

Superscalar Technique (Option A): Uses multiple execution units so that several instructions can be issued and executed in parallel within a single CPU cycle.

VLIW Technique (Option B): Uses very long instruction words, where multiple operations are encoded into a single instruction and executed in parallel.

SuperVector (Option C): Refers to vector processors, which process large arrays of data but is not classified as a mainstream architectural parallel technique in modern CPU design.

Therefore, the primary architectural techniques for parallel processing are Superscalar and VLIW, making Option D (Both A and B) correct.

Time Series Analysis (TSA) is the process of analyzing data collected sequentially over time to extract meaningful insights.Applications include:

Option A: Correct. Event prediction (e.g., failure detection in IoT or predictive maintenance).

Option B: Correct. Forecasting future values (e.g., stock price, sales forecasting).

Option C: Correct. Pattern discovery in large-scale time series datasets using clustering, anomaly detection, or seasonality detection.

Since all three are true, the best answer is Option 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).

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).

LaTeX is a high-quality typesetting system widely used in academia, particularly in scientific publishing.

Statement i: Correct. LaTeX is widely used to prepare manuscripts for scientific journals, theses, and technical reports.

Statement ii: Correct. LaTeX is a markup language (similar to HTML in concept) that compiles into formatted PDFs/documents.

Statement iii: Correct. LaTeX is a standard for publishing scientific papers due to its ability to handle complex mathematical equations, references, and formatting.

Thus, all three statements are true → Option B (i, ii, iii).

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).

Apache Avro is a widely used framework within the Hadoop ecosystem for data serialization and data exchange.

Option A (Correct): Avro is a compact, fast, binary data serialization format. It allows efficient storage and exchange of structured data.

Option B (Correct): Avro supports Remote Procedure Call (RPC). It provides a framework for RPC communication, making it easier for distributed applications to exchange data across systems.

Option C: Correct, since both statements are true.

Option D: Incorrect because Avro is indeed both a serialization framework and RPC-based.

In data engineering workflows, Avro is valuable because it is schema-based (defined using JSON), highly interoperable, and ensures compatibility across different programming languages. This makes it essential in big data pipelines, Kafka messaging, and Hadoop ecosystem tools.

Thus, the correct answer is Option C (Both A and B).

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).

The Chief Data Monetization Officer (CDMO) is a C-level executive role emerging in data-driven organizations. Their focus is on turning data into business value and revenue growth.

Statement i: Correct. The CDMO is responsible for value realization from data and analytics investments.

Statement ii: Correct. The CDMO owns decisions around investment prioritization for data initiatives.

Statement iii: Correct. Since monetization is directly linked to business performance, the CDMO must be accountable for revenue and margin impact.

Hence, all three statements are correct, making the right answer Option C (All of the above).

A Markov chain is a stochastic process describing a sequence of possible events, where the probability of each event depends only on the state attained in the previous step (the Markov property).

Option A: Incorrect. The variables are random, not fixed.

Option B: Correct. Markov chains represent the state of a system at sequential time points.

Option C: Correct. A Markov chain is indeed a sequence of random variables {X1, X2, …} that satisfy the Markov property.

Option D: Incorrect, since A is wrong.

Option E: Correct, because both B and C are valid.

Thus, the correct answer is Option E (Both B and C).

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).

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.

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).

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).

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.

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).

Grid computing is a distributed computing model where resources (CPU, memory, storage) are pooled across multiple systems to solve large-scale problems.

Option A (Divide): Middleware helps allocate or divide resources dynamically to different tasks.

Option B (Combine): Middleware integrates diverse resources into a unified system, making them accessible for parallel computing.

Option C: Correct — middleware is the “glue” that enables both combining and dividing resources seamlessly across distributed nodes.

Option D: Incorrect.

Thus, grid computing middleware both combines and divides resources, making Option C correct.

Regression is a supervised learning technique where a model estimates the relationship between input features (independent variables) and an output (dependent variable).

Option A: Correct. The learning process involves optimizing model parameters (e.g., coefficients in linear regression) to minimize approximation error. Common loss functions include Mean Squared Error (MSE) or Mean Absolute Error (MAE).

Option B: Correct. Minimizing error enables the model to produce the closest possible outcomes to the actual observed values, ensuring accurate predictions.

Option C: Correct, since both A and B are true.

Option D: Incorrect.

Thus, regression optimization in machine learning aims to minimize approximation error and generate closest possible outcomes, making Option C the correct answer.

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 hypothesis testing, two main types of errors are defined:

Type I Error (Option A): Occurs when the null hypothesis (H₀) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.

Type II Error (Option B): Occurs when the null hypothesis (H₀) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.

Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.

Thus, the “wrong negation of a true null hypothesis” refers to a Type I Error (false positive).

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).

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.