Q#1: Data mining is primarily used for:
(A) Extracting patterns and knowledge from large datasets
(B) Only backup operations
(C) Index optimization
(D) Normalization
Answer: (A) Extracting patterns and knowledge from large datasets
Q#2: KDD in data mining stands for:
(A) Knowledge Discovery in Databases
(B) Key Data Definition
(C) Knowledge Data Distribution
(D) Key Database Design
Answer: (A) Knowledge Discovery in Databases
Q#3: The main steps in KDD include:
(A) Data selection, cleaning, transformation, mining, interpretation
(B) Backup, indexing, normalization
(C) Encryption, compression, backup
(D) Only querying data
Answer: (A) Data selection, cleaning, transformation, mining, interpretation
Q#4: Classification in data mining refers to:
(A) Assigning items to predefined categories
(B) Grouping similar items without predefined labels
(C) Only backup operations
(D) Index optimization
Answer: (A) Assigning items to predefined categories
Q#5: Clustering in data mining refers to:
(A) Grouping similar items without predefined labels
(B) Assigning items to known classes
(C) Only indexing
(D) Backup only
Answer: (A) Grouping similar items without predefined labels
Q#6: Association rule mining is used for:
(A) Discovering relationships between variables in large datasets
(B) Classification only
(C) Clustering only
(D) Backup only
Answer: (A) Discovering relationships between variables in large datasets
Q#7: A common application of association rules is:
(A) Market basket analysis
(B) Data backup
(C) Index creation
(D) Normalization
Answer: (A) Market basket analysis
Q#8: Predictive data mining focuses on:
(A) Predicting unknown values or trends based on known data
(B) Only backups
(C) Indexing only
(D) Data encryption
Answer: (A) Predicting unknown values or trends based on known data
Q#9: Descriptive data mining focuses on:
(A) Finding patterns and relationships in existing data
(B) Predicting future trends
(C) Backup only
(D) Encryption only
Answer: (A) Finding patterns and relationships in existing data
Q#10: Regression in data mining is used for:
(A) Predicting continuous numeric values
(B) Predicting categorical classes
(C) Clustering data only
(D) Backup only
Answer: (A) Predicting continuous numeric values
Q#11: Decision trees in data mining are used for:
(A) Classification and prediction
(B) Only clustering
(C) Only backup
(D) Index optimization
Answer: (A) Classification and prediction
Q#12: Data mining requires:
(A) Large, accurate, and relevant datasets
(B) Only small datasets
(C) Only indexes
(D) Backup only
Answer: (A) Large, accurate, and relevant datasets
Q#13: Outlier detection in data mining helps to:
(A) Identify unusual or abnormal data points
(B) Backup data
(C) Index optimization
(D) Normalize tables
Answer: (A) Identify unusual or abnormal data points
Q#14: Association rules use metrics such as:
(A) Support and confidence
(B) Backup and restore
(C) Index and key
(D) Normalization and clustering
Answer: (A) Support and confidence
Q#15: Data mining can be applied in:
(A) Banking, healthcare, marketing, fraud detection
(B) Only backups
(C) Index optimization
(D) Normalization
Answer: (A) Banking, healthcare, marketing, fraud detection
Q#16: Supervised learning in data mining requires:
(A) Labeled datasets
(B) Unlabeled datasets
(C) Backup files
(D) Index files
Answer: (A) Labeled datasets
Q#17: Unsupervised learning in data mining uses:
(A) Unlabeled datasets
(B) Labeled datasets only
(C) Backup files
(D) Index files
Answer: (A) Unlabeled datasets
Q#18: Popular clustering algorithms include:
(A) K-means, DBSCAN, Hierarchical clustering
(B) Decision trees only
(C) Regression only
(D) Backup scripts only
Answer: (A) K-means, DBSCAN, Hierarchical clustering
Q#19: Popular classification algorithms include:
(A) Decision trees, Naive Bayes, SVM, Neural networks
(B) K-means only
(C) DBSCAN only
(D) Backup scripts only
Answer: (A) Decision trees, Naive Bayes, SVM, Neural networks
Q#20: Data preprocessing in data mining includes:
(A) Cleaning, integration, transformation, reduction
(B) Backup only
(C) Indexing only
(D) Encryption only
Answer: (A) Cleaning, integration, transformation, reduction
Q#21: Data mining can handle:
(A) Structured, semi-structured, and unstructured data
(B) Only structured data
(C) Only backup files
(D) Only indexes
Answer: (A) Structured, semi-structured, and unstructured data
Q#22: Benefits of data mining include:
(A) Knowledge discovery, decision support, predictive analysis
(B) Only backup
(C) Only indexing
(D) Only normalization
Answer: (A) Knowledge discovery, decision support, predictive analysis
Q#23: Text mining is a type of:
(A) Data mining applied to textual data
(B) Backup operation
(C) Indexing only
(D) Encryption only
Answer: (A) Data mining applied to textual data
Q#24: Web mining focuses on:
(A) Extracting useful information from web data
(B) Only backups
(C) Indexing only
(D) Normalization only
Answer: (A) Extracting useful information from web data
Q#25: Data mining can improve:
(A) Business decision-making and strategic planning
(B) Only backups
(C) Only indexing
(D) Normalization only
Answer: (A) Business decision-making and strategic planning
Q#26: Data mining risks include:
(A) Privacy violations, data misuse, and overfitting
(B) Only backup failure
(C) Only indexing errors
(D) Normalization issues
Answer: (A) Privacy violations, data misuse, and overfitting
Q#27: Frequent pattern mining is used to:
(A) Identify recurring relationships in data
(B) Backup data only
(C) Index only
(D) Normalize tables only
Answer: (A) Identify recurring relationships in data
Q#28: Cross-validation in data mining is used to:
(A) Evaluate the performance of predictive models
(B) Backup only
(C) Index only
(D) Encrypt data only
Answer: (A) Evaluate the performance of predictive models
Q#29: Data mining can be combined with:
(A) Machine learning, statistics, and databases
(B) Only backups
(C) Only indexing
(D) Only normalization
Answer: (A) Machine learning, statistics, and databases
Q#30: Main goal of data mining is:
(A) Discover hidden patterns and knowledge from data to support decision-making
(B) Backup only
(C) Indexing only
(D) Encryption only
Answer: (A) Discover hidden patterns and knowledge from data to support decision-making