Đề 7 - Bài tập, đề thi trắc nghiệm online Khoa học dữ liệu trong kinh tế và kinh doanh
1. How can data science contribute to innovation in new product development?
A. By solely relying on traditional market research methods.
B. By analyzing customer data and market trends to identify unmet needs and opportunities.
C. By ignoring customer feedback and focusing only on competitor products.
D. By making product development decisions based on gut feeling and intuition.
2. In operations management, how can predictive analytics be used to reduce operational costs?
A. By increasing inventory levels to prevent any stockouts.
B. By forecasting equipment failures and optimizing maintenance schedules.
C. By ignoring potential disruptions and assuming smooth operations.
D. By solely relying on reactive maintenance after equipment breaks down.
3. What is the 'curse of dimensionality′ in machine learning, and why is it relevant in data science?
A. It refers to the lack of sufficient data for model training.
B. It refers to the challenges arising when dealing with data in very high-dimensional spaces.
C. It refers to the ease of interpreting machine learning models.
D. It refers to the low computational cost of processing large datasets.
4. In finance, how can data science be applied to risk management?
A. By eliminating all risks associated with financial investments.
B. By developing models to predict and assess potential financial risks.
C. By solely relying on historical financial reports without data analysis.
D. By ignoring market volatility and focusing on long-term averages only.
5. In the context of e-commerce, how can recommendation systems, powered by data science, increase sales?
A. By randomly showing products to customers without any personalization.
B. By suggesting products that are highly relevant and appealing to individual customers.
C. By decreasing the variety of products offered to simplify choices.
D. By making the online shopping experience more confusing and less user-friendly.
6. What is the role of data visualization in communicating data science findings to business stakeholders?
A. To make data analysis more complex and harder to understand.
B. To present complex data insights in a clear, accessible, and impactful manner.
C. To replace statistical analysis with visually appealing graphics.
D. To hide the limitations and uncertainties of data analysis.
7. In supply chain management, how can data science optimize logistics?
A. By manually tracking each shipment without using predictive models.
B. By ignoring real-time data and relying only on historical averages.
C. By predicting demand, optimizing routes, and improving inventory management.
D. By increasing transportation costs to ensure faster delivery times.
8. What is the significance of 'interpretable machine learning′ in business applications?
A. Interpretability is not important as long as the model is accurate.
B. It allows stakeholders to understand how and why a model makes certain predictions or decisions.
C. It makes models more complex and harder to understand.
D. It reduces the accuracy of machine learning models.
9. How can data science assist in market segmentation for businesses?
A. By treating all customers as a single homogeneous group.
B. By identifying distinct groups of customers with similar needs and preferences.
C. By ignoring customer demographics and focusing solely on product features.
D. By making marketing campaigns less targeted and more generic.
10. Which of the following is NOT typically considered a core component of the data science process in business?
A. Data collection and cleaning.
B. Model building and evaluation.
C. Implementation of insights into business decisions.
D. Ignoring the business context and focusing solely on technical aspects.
11. In fraud detection within financial institutions, how is data science typically employed?
A. By manually reviewing each transaction for suspicious activity.
B. By using algorithms to identify patterns indicative of fraudulent transactions.
C. By ignoring historical transaction data and focusing only on current events.
D. By relying solely on rule-based systems without machine learning.
12. What is a potential drawback of relying too heavily on data science models for business decisions?
A. Data science models are always perfectly accurate and unbiased.
B. Over-reliance can lead to neglecting qualitative insights and human judgment.
C. Data science models are too simple to capture real-world complexities.
D. Data science models are inexpensive and easy to implement in all situations.
13. Which type of business question is BEST addressed by 'clustering′ techniques in data mining?
A. Predicting the future stock price of a company.
B. Identifying distinct groups of customers based on their purchasing behavior.
C. Determining the exact cause of a specific business event.
D. Automating customer service responses using chatbots.
14. What is 'Big Data′ primarily characterized by in the context of economics and business?
A. Data that is small and easily manageable with traditional tools.
B. Data characterized by its Volume, Velocity, Variety, and Veracity (the 4 Vs).
C. Data that is exclusively numerical and structured in databases.
D. Data that is processed and analyzed manually without computational tools.
15. What is 'regression analysis′ in data science, and when is it typically used in business?
A. A technique for grouping data points into clusters.
B. A technique for predicting a continuous numerical output variable.
C. A technique for classifying data into discrete categories.
D. A technique for discovering association rules in transactional data.
16. What is the role of 'data governance′ in ensuring the effective use of data science in business?
A. To restrict access to data and limit data sharing within the organization.
B. To establish policies and procedures for data quality, security, and ethical use.
C. To focus solely on data collection without considering data management.
D. To eliminate all regulations and restrictions on data usage for innovation.
17. Which of the following BEST describes the role of a 'Data Scientist′ in an organization?
A. Primarily responsible for managing IT infrastructure and hardware.
B. Focused on cleaning and preparing data, but not analyzing it.
C. Responsible for extracting insights from data, building models, and communicating findings to stakeholders.
D. Exclusively focused on data entry and data storage.
18. How can data science contribute to improving customer relationship management (CRM)?
A. By making customer interactions less personalized and more generic.
B. By analyzing customer data to personalize interactions and predict customer needs.
C. By reducing the amount of customer data collected to simplify analysis.
D. By ignoring customer feedback and focusing only on sales metrics.
19. In the context of data science projects, what does 'model deployment′ typically involve?
A. Building a machine learning model but not using it for real-world applications.
B. Integrating a trained model into a business system to automate predictions or decisions.
C. Keeping the model in a research environment without practical implementation.
D. Discarding the model after evaluation and starting a new project.
20. What is a potential ethical concern associated with using data science in human resources (HR) for employee performance evaluation?
A. Data science in HR always leads to perfectly objective and fair evaluations.
B. Algorithms used in HR might inadvertently perpetuate or amplify existing biases against certain groups.
C. HR data is always readily available and ethically sourced.
D. Employees are always fully aware of and consent to data collection for performance evaluation.
21. What is the purpose of 'hypothesis testing′ in data science for business research?
A. To confirm existing assumptions without empirical evidence.
B. To statistically evaluate the validity of a claim or hypothesis using data.
C. To avoid making any conclusions based on data analysis.
D. To make business decisions based purely on intuition.
22. Which of the following BEST describes the primary goal of data science in economics and business?
A. To replace human decision-making entirely with automated systems.
B. To collect and store vast amounts of data, regardless of its relevance.
C. To extract valuable insights and knowledge from data to support informed decision-making.
D. To create complex algorithms for the sake of technological advancement.
23. Which of these scenarios BEST exemplifies the application of data science in marketing?
A. Using intuition to design a new advertising campaign.
B. Randomly sending out promotional emails to all customers.
C. Analyzing customer purchase history to personalize product recommendations.
D. Ignoring customer feedback and focusing solely on competitor strategies.
24. Which of these is an example of 'unstructured data′ that businesses might analyze using data science techniques?
A. Customer purchase transaction records in a database.
B. Social media posts and customer reviews.
C. Spreadsheet data of monthly sales figures.
D. Financial statements in a standardized format.
25. What is 'natural language processing′ (NLP), and how is it relevant to data science in business?
A. A method for processing numerical data only.
B. A field focused on enabling computers to understand and process human language.
C. A technique for visualizing data in charts and graphs.
D. A tool for managing databases and structured data.
26. What is the potential impact of data science on economic inequality?
A. Data science will automatically eliminate economic inequality.
B. Data science applications could potentially exacerbate or alleviate economic inequality depending on their implementation and ethical considerations.
C. Data science has no relation to economic inequality.
D. Economic inequality is solely determined by factors unrelated to technology.
27. Which of the following BEST describes the concept of 'data-driven culture′ in an organization?
A. A culture where decisions are primarily based on intuition and experience.
B. A culture where data is collected but not actively used for decision-making.
C. A culture where data and analytics are central to decision-making processes at all levels.
D. A culture where only the IT department is concerned with data.
28. What is 'feature engineering′ in the context of data science?
A. The process of selecting the most advanced algorithms for data analysis.
B. The process of directly using raw data without any modification.
C. The process of transforming raw data into features that are suitable for machine learning models.
D. The process of ignoring irrelevant data points to simplify analysis.
29. What is 'A∕B testing′, and how is it used in data-driven business decisions?
A. A method to avoid any experimentation and rely solely on existing data.
B. A method to test two or more versions of a variable to see which performs better.
C. A method to make decisions based on gut feeling rather than data.
D. A method to confirm pre-existing biases without empirical evidence.
30. In the context of business analytics, what is the MAIN difference between descriptive analytics and predictive analytics?
A. Descriptive analytics focuses on future outcomes, while predictive analytics analyzes past data.
B. Descriptive analytics summarizes historical data, while predictive analytics forecasts future trends.
C. Descriptive analytics is qualitative, while predictive analytics is quantitative.
D. Descriptive analytics uses complex algorithms, while predictive analytics relies on simple statistics.
