DASCA Senior Data Scientist SDS Exam Dumps: Updated Questions & Answers (October 2025)

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).

Data wrangling (also called data munging) refers to transforming raw, messy, or unstructured data into a clean and structured format suitable for analysis.

Option A: Correct. Raw data often contains missing values, duplicates, or irregular formats. Wrangling prepares it for conventional analytics and machine learning.

Option B: Incorrect. Wrangling does not involve "modified meaning"; it focuses on cleaning, structuring, and integrating.

Option C: Incorrect, since only A is correct.

Option D: Incorrect, because wrangling is explicitly described in A.

Thus, the correct answer is Option A.

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).

DevOps is a collaborative practice that integrates software development (Dev) and IT operations (Ops) to shorten development cycles and deliver applications reliably. Common DevOps practices include:

Continuous Build (Option A): Automating compilation and packaging of source code to ensure consistent builds.

Continuous Integration (Option B): Developers frequently merge code into a shared repository, which is automatically tested to catch integration issues early.

Continuous Delivery (Option C): Automating software release pipelines so applications can be deployed to production quickly and reliably.

Since all of these are essential DevOps practices, the correct answer is Option D (All of the above).

Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.

Typical components of IPO include:

Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.

Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.

Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.

However:

Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.

Therefore, the correct answer is Option B (Business Metamorphosis).

A Statement of Work (SOW) is a formal document that defines the scope, objectives, deliverables, timeline, and expectations of a project. In data science and IT projects, it ensures:

Clarity of scope (Option A): Everyone understands exactly what work should be done.

Clear priorities (Option B): It defines what is most critical for success.

Realistic expectations (Option C): It aligns stakeholders by setting measurable and achievable goals.

Since all of these are essential purposes of an SOW, the correct answer is Option D (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.

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).

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).

But per MCQ single-choice format → Answer: A (though ideally A, B, C are correct).

A data lake is a centralized repository designed to store raw, structured, semi-structured, and unstructured data at scale. It provides:

Agility and productivity (Option A): Data lakes support flexible ingestion and faster access, making BI and data science environments more efficient.

Data integration (Option B): They handle multiple types of data, enabling advanced analytics and machine learning use cases.

Data as an asset (Option C): They shift perspective, treating data as a strategic resource, not just a storage cost.

Option D: Incorrect. Data lakes improve agility, not reduce it.

Option E: Incorrect, since multiple true statements exist.

Thus, the correct choice per DASCA context is Option A (with B and C also being true).