Pass the DASCA Data Scientist SDS Questions and answers with ExamsMirror

The DevOps movement evolved as a natural extension of the Agile methodology.

Agile (Option A): Agile emphasizes iterative development, collaboration, and flexibility. While Agile improved software development speed, it created challenges in integrating development with IT operations. DevOps emerged to address this by bringing operations into the Agile cycle — enabling continuous integration, delivery, and deployment.

Waterfall (Option B): Incorrect. Waterfall is a rigid, sequential methodology, fundamentally opposite to the DevOps philosophy.

Promise-based algorithms (Option C): Not a methodology — irrelevant here.

Test-driven development and model-driven development (Option D): While these practices support DevOps, they are not the origin of the movement.

Thus, the DevOps movement is an outgrowth of Agile methodology.

The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.

Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.

Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.

Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).

Thus, all three statements are valid, making the correct answer Option E (All of the above).

Business Metamorphosis is the most advanced phase in the Big Data Business Model Maturity Index (BDBMMI), where organizations fundamentally transform their business models through analytics-driven insights.

Option A: Correct. This phase helps organizations identify big data requirements related to decisions, analytics, and sources that drive business transformation.

Option B: Correct. Organizations integrate customer usage patterns, product behaviors, and market trends into their decision-making to redesign or innovate their business model.

Option C: Correct. Business Metamorphosis ensures that the core business model evolves continuously, guided by insights derived across maturity stages.

Since all are correct, the best answer is Option E (All of the above).

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).

Pandas is one of the most widely used Python libraries for data analysis and manipulation. Its central object is the DataFrame.

Option A: Correct. DataFrame is the core data structure in Pandas.

Option B: Correct. DataFrame resembles SQL tables and R data frames, supporting row/column indexing, joins, and grouping.

Option C: Correct. A DataFrame is essentially a 2D labeled table consisting of rows and columns.

Option D: Correct, but not fully inclusive.

Option E: Correct, since all of A, B, and C are true.

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

A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.

Option A: Incorrect. Burn down charts don’t measure team “energy” or motivation levels.

Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.

Option C: Incorrect. Hours worked after dark is irrelevant.

Option D: Incorrect. Budget reduction is not tracked in burn down charts.

Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.

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.

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.

A Box Plot (also called Whisker Plot) is a visualization tool used to summarize data distribution using five-number summary:

Minimum,

First quartile (Q1),

Median (Q2),

Third quartile (Q3),

Maximum.

It also highlights outliers explicitly.

Option A (Box Plots): Correct.

Option B (Pie Charts): Show proportions, not distribution.

Option C (Histogram): Shows frequency distribution but not quartiles/median.

Option D (Scatter Chart): Used for relationships between two variables, not summary statistics.

Option E (Bar Charts): Compare categories, not statistical spread.

Thus, the correct answer is Option A (Box Plots).

Optical Character Recognition (OCR) is the process of automatically recognizing and converting different types of documents — such as scanned paper documents, PDFs, or images — into editable and searchable text.

OCR systems use Machine Learning (ML) and Computer Vision techniques to detect and classify patterns of characters in images.

Algorithms like Convolutional Neural Networks (CNNs) are commonly used for image-based OCR.

While OCR may indirectly contribute to data mining or big data workflows, the core application is based on machine learning, where models are trained to classify and recognize text patterns.

Thus, OCR is primarily a Machine Learning application, making Option B correct.