SDS Exam Dumps - DASCA Data Scientist Questions and Answers

Neural networks have been widely applied in various domains:

Option A (Character recognition): Correct application — neural networks are highly effective for OCR (Optical Character Recognition).

Option B (Stock market prediction): Correct application — neural networks are used to model time-series and nonlinear patterns in finance.

Option D (Image compression): Correct application — neural nets (autoencoders) are used for dimensionality reduction and compression.

Option C (Traveling salesman’s problem): NOT a typical neural network application. This is a combinatorial optimization problem usually solved with heuristics, dynamic programming, or optimization algorithms (not standard neural networks).

Thus, the correct answer is Option C (Traveling salesman’s problem).

Graphical models are probabilistic models that represent variables and dependencies using graphs:

Markov Random Fields (Option A): Undirected graphical models that capture joint distributions over variables with neighborhood dependencies.

Bayesian Networks (Option B): Directed acyclic graphical models that encode conditional dependencies between random variables.

Geographical Networks (Option C): While they are graphs, they are not probabilistic graphical models used in statistics/ML.

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

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.

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.

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.

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).

"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.

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 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).