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Computer Fundamentals → Introduction to Computer 38

Apache Spark is faster than MapReduce because?
In-memory processing instead of disk I/O between stages
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Directed Acyclic Graph (DAG) in Spark represents?
Task execution plan with dependencies
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Stream processing vs batch processing: stream processes?
Data in real time as it arrives
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Apache Kafka is a?
Distributed event streaming platform (message queue)
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Kafka topic partition allows?
Parallel consumption and horizontal scaling
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Event sourcing pattern stores?
All state changes as immutable sequence of events
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CQRS (Command Query Responsibility Segregation) separates?
Read (query) and write (command) data models
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Saga pattern in microservices handles?
Distributed transactions through sequence of local transactions with compensations
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Circuit breaker pattern in microservices?
Prevents cascading failures by stopping requests to failing service
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Service discovery in microservices allows?
Dynamic detection of service instances and their locations
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Which algorithm detects cycles in a directed graph?
DFS with coloring (White-Gray-Black)
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Articulation point in a graph is?
Vertex whose removal disconnects the graph
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Tarjan's algorithm finds?
Strongly Connected Components (SCCs) in directed graph
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Kosaraju's algorithm for SCCs performs how many DFS passes?
2
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Maximum flow problem solution includes?
Ford-Fulkerson / Edmonds-Karp algorithm
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Fenwick tree (Binary Indexed Tree) is used for?
Efficient prefix sum queries and point updates in O(log n)
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Segment tree supports?
Range queries and range updates in O(log n)
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Sparse table is used for?
Static range minimum/maximum queries in O(1) after O(n log n) preprocessing
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Disjoint Set Union (DSU/Union-Find) with path compression and union by rank achieves?
Near O(1) amortized per operation (inverse Ackermann function)
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Knuth-Morris-Pratt (KMP) string matching algorithm runs in?
O(n+m) using failure function
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Gradient descent optimization minimizes?
Loss function by iteratively adjusting weights
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Mini-batch gradient descent uses?
Small random subset of samples per update
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Adam optimizer combines?
RMSprop and Momentum — adaptive learning rates per parameter
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Learning rate in neural networks?
Controls step size in weight updates during training
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Dropout regularization randomly?
Deactivates random neurons during training to prevent overfitting
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Batch normalization normalizes?
Activations within each mini-batch, stabilizing training
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Transfer learning uses?
Pre-trained model weights as starting point for new task
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Attention mechanism in transformers computes?
Weighted sum of values based on query-key similarity
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Self-attention in transformers?
Computes attention within the same sequence — every token attends to all others
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GPT models are?
Auto-regressive — generate text left to right using decoder-only transformer
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Which evaluation metric is used for classification with imbalanced classes?
F1 score (harmonic mean of precision and recall)
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Precision in classification is?
TP/(TP+FP) — of all predicted positive, how many are actually positive
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Recall (Sensitivity) is?
TP/(TP+FN) — of all actual positives, how many were correctly identified
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ROC curve plots?
True Positive Rate vs False Positive Rate at various thresholds
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AUC (Area Under ROC Curve) of 1.0 means?
Perfect classifier
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K-fold cross-validation partitions data into?
K folds — train on K-1, validate on 1, repeat K times
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Which ML algorithm is based on Bayes' theorem?
Naïve Bayes classifier
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Support Vector Machine (SVM) finds?
Maximum margin hyperplane separating classes
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Computer Fundamentals → Memory Units 2

Stochastic Gradient Descent (SGD) differs from batch GD in?
Updates weights using single random sample per iteration
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BERT (Bidirectional Encoder Representations from Transformers) is pre-trained using?
Masked Language Modeling (MLM) and Next Sentence Prediction (NSP)
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