exam samples questions prepared by CCSD Council on big data analytics

Creating sample exam questions for a Council on Big Data Analytics, such as CCSD, involves a combination of theoretical understanding and practical application. Here are some sample questions in various formats that could be used in an examination setting: ### Multiple Choice Questions 1. **Which of the following is not a characteristic of big data?** - A) Volume - B) Velocity - C) Variability - D) Veracity **Answer:** C) Variability 2. **In the context of data mining, what does the term "clustering" refer to?** - A) Grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. - B) Reducing the dimensionality of data to simplify analysis. - C) Predicting a continuous outcome based on one or more predictor variables. - D) Transforming data into a more convenient format for analysis. **Answer:** A) Grouping a set of objects... ### True/False Questions 3. **True or False:** Big data analytics can only be conducted using cloud-based platforms. **Answer:** False 4. **True or False:** The term "data lake" refers to a centralized repository that allows you to store all your structured and unstructured data at any scale. **Answer:** True ### Short Answer Questions 5. **Explain the concept of "data wrangling" and its importance in big data analytics.** **Answer:** Data wrangling is the process of cleaning, restructuring, and enriching raw data into a desired format for better decision-making in analysis. It is crucial because raw data is often incomplete or inconsistent, and transforming it into a more usable format ensures accuracy in subsequent analyses. 6. **What are the primary tools used for big data analytics? Provide at least three examples.** **Answer:** Examples of primary tools include: - Apache Hadoop: A framework for processing large datasets. - Apache Spark: A fast and general-purpose cluster computing system. - Tableau: A visualization tool for business intelligence. ### Case Study Questions 7. **Case Study:** A retail company wants to improve its inventory management using big data analytics. Describe two analytical approaches the company could take and explain how these approaches would benefit their inventory management. **Answer:** - **Predictive Analytics:** The company could use historical sales data to predict future demand for products. By anticipating sales trends, the company can optimize inventory levels and reduce overstock or stockouts. - **Real-Time Analytics:** Implementing real-time analytics can help monitor inventory levels and sales in real-time, allowing for dynamic inventory adjustments and more timely reordering. ### Essay Questions 8. **Discuss the ethical implications of big data analytics in consumer privacy. What measures can organizations take to ensure they are using data responsibly?** **Answer:** (Expected to discuss concerns about data ownership, consent, data breaches, and how companies might anonymize data, implement data governance frameworks, seek user consent, and comply with regulations such as GDPR.) 9. **Evaluate the impact of machine learning on big data analytics. How do machine learning algorithms enhance traditional analytical methods?** **Answer:** (Expected to cover the interplay between machine learning and big data, addressing the scalability of machine learning algorithms with massive datasets, their ability to identify patterns, and automated insights generation compared to traditional statistical methods.) These questions aim to assess various aspects of big data analytics, from theoretical knowledge to practical applications, and encourage critical thinking among students.