Challenges in mining large datasets MCQs December 22, 2025November 19, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is one of the main challenges in handling big data in data mining? (A) Difficulty in ensuring data privacy and security (B) High cost of computing power (C) Lack of storage space (D) Inability to process small datasets 2. Which challenge is most commonly associated with noisy data in large datasets? (A) Data quality assurance (B) Correctly interpreting data (C) Inability to store the data (D) Data becomes more dense and complex 3. In data mining for large datasets, what does scalability refer to? (A) The ability of a dataset to grow (B) The efficiency of algorithms when processing small datasets (C) The process of making the dataset more uniform (D) The capability of handling increasing data volume without performance degradation 4. Which of the following is a significant challenge in applying data mining to large datasets? (A) The ability to visualize small datasets (B) The amount of irrelevant or redundant data (C) The simplicity of algorithms (D) The need for a small amount of data storage 5. What is a challenge related to high-dimensionality in large datasets? (A) The inability to process data in parallel (B) It reduces the storage requirements for data (C) Data becomes too small to handle (D) The difficulty in identifying meaningful relationships between features 6. Which issue occurs when large datasets are distributed across multiple machines or servers? (A) Reduced complexity in mining (B) Increased accuracy of results (C) Data synchronization and consistency issues (D) Lack of available algorithms to handle distributed data 7. What does the “curse of dimensionality” refer to? (A) The problem of generating high-quality datasets from low-dimensional data (B) The challenge of integrating low-dimensional datasets (C) The ability to easily visualize high-dimensional data (D) The difficulty of working with high-dimensional data due to increased complexity 8. What is a key challenge when working with streaming data in large datasets? (A) Real-time processing of continuously generated data (B) Storing data for future analysis (C) Reducing the complexity of data (D) Visualizing data after it is collected 9. What challenge arises from the presence of missing values in large datasets? (A) They do not impact data mining processes significantly (B) They increase the complexity of data storage (C) They complicate the model training and result interpretation (D) They can lead to a higher accuracy in predictions 10. Which of the following is a challenge associated with big data analytics? (A) Insufficient data variety (B) Decreased need for advanced algorithms (C) Increased computational cost and time (D) Low-speed data processing 11. How does data quality affect the performance of data mining on large datasets? (A) Poor data quality complicates the identification of valuable insights (B) High-quality data makes mining faster and more effective (C) Data quality does not impact mining in large datasets (D) Poor data quality leads to more accurate models 12. Which challenge arises from real-time data mining in large datasets? (A) Difficulty in processing batch data (B) Inability to store data for offline analysis (C) The need for powerful, real-time algorithms (D) Reduced model accuracy due to real-time constraints 13. What is the main challenge in parallel data mining on large datasets? (A) Achieving high performance across distributed systems (B) Finding enough data to mine in parallel (C) Ensuring each process operates independently without overlap (D) Using simple algorithms for parallel execution 14. In data mining, which issue arises when handling heterogeneous data sources? (A) Data is always consistent and easy to combine (B) Different formats and structures make integration difficult (C) It leads to faster processing times (D) It improves the quality of insights 15. What challenge does model interpretability present in large datasets? (A) More complex models are easier to interpret (B) High-dimensional data makes models simpler (C) Models are always simple and interpretable (D) Complex models are harder to interpret despite accuracy