All MCQs

The population is the complete set of units about which the researcher wants to draw conclusions. Survey respondents form a sample unless every eligible student is included.

Computational thinking decomposes a problem into logical components that can be represented and automated. The reusable steps form a structured solution process.

Data scientists must explain methods, uncertainty, and practical consequences to nontechnical stakeholders. Technical correctness alone does not ensure that results will guide decisions.

Some applications prioritize forecasting future outcomes rather than proving why those outcomes occur. Predictive usefulness does not by itself demonstrate a cause-and-effect relationship.

Hypothesis-driven analysis starts with a proposed explanation or relationship and evaluates evidence for it. The researcher is testing a predefined claim rather than searching without an initial theory.

A data-science pipeline connects the major stages from data acquisition through operational monitoring. Each stage contributes to producing and maintaining a usable solution.

Predictive modeling requires learning patterns and testing how well they generalize to unseen cases. Routine reporting mainly summarizes information using a predefined process.

Value is the practical benefit or insight obtained from data. Size alone does not make data useful unless it supports a meaningful outcome.

Veracity concerns the reliability, quality, and trustworthiness of data. Errors and uncertain timestamps reduce confidence in the measurements.

Variety refers to the presence of multiple data formats, structures, and sources. The project includes tabular, visual, audio, and streaming data.

Velocity concerns the speed at which data is generated, received, and processed. Real-time fraud detection depends on rapid analysis of incoming transactions.

Volume refers to the quantity or scale of data being stored and processed. Petabytes indicate an exceptionally large amount of data.