HTTP is a stateless protocol and is connectionless, meaning it does not maintain a persistent connection between requests.

K-means clustering struggles with non-spherical clusters, as it assumes clusters are spherical and equally sized.

Linear regression can handle multiple predictors, not just two variables.

The greedy choice property is not a characteristic of problems suitable for dynamic programming; it is a characteristic of greedy algorithms.

Quick Sort is not a stable sorting algorithm, meaning it does not guarantee the relative order of equal elements.

While Random Forests are robust, they do not always guarantee the best accuracy for every dataset.

RNNs are not always faster than feedforward networks; their sequential nature can lead to longer training times.

Supervised learning requires labeled data and does not automatically discover patterns without supervision.

SVM can be adapted for multi-class classification using strategies like one-vs-one or one-vs-all, so it is not limited to binary classification.

Predictive modeling is not a common application of clustering methods, as it typically involves supervised learning rather than unsupervised clustering.

Image classification typically involves supervised learning, while clustering is unsupervised.

Training new models is not an application of deployed models; rather, it is part of the model development process.

SVM is primarily used for classification tasks, while clustering is typically handled by different algorithms.

Hyperparameter tuning is typically a part of the model training process, not a challenge faced during model deployment.

Mean squared error is typically used in regression tasks, while the other options are used for classification tasks.

Random deployment is not a common deployment strategy; the others (Blue-Green, Canary, and Rolling) are widely used.

Logistic distance is not a standard distance metric used in clustering; common metrics include Euclidean, Manhattan, and Cosine similarity.

Mean Squared Error (MSE) is primarily used for regression models, while Precision, Recall, and F1 Score are metrics used for evaluating classification models.

Training loss is not used for evaluating deployed models; it is a measure of performance during training, not in production.

Machine code is the final output of a compiler, while three-address code, abstract syntax trees, and bytecode are forms of intermediate code.

Hierarchical initialization is not a common method for initializing K-means; the other three are standard techniques.

Logistic kernel is not a standard kernel used in SVM; common kernels include linear, polynomial, and RBF.

Data warehousing is not a common method for deploying machine learning models; instead, models are typically deployed via REST APIs, batch processing, or embedded systems.

Data versioning is not a monitoring method; it is a practice used to manage changes in datasets over time.

Feature selection is the process of selecting a subset of relevant features, while Min-Max Scaling, Standardization, and Log Transformation are techniques for feature scaling.

Gradient Descent is an optimization algorithm, not a feature selection technique.

Cross-validation is a model evaluation technique, not a feature engineering technique.

Gradient Descent is an optimization algorithm, not a feature selection technique.

Image classification is typically a supervised learning task, while clustering is used for market segmentation, anomaly detection, and social network analysis.

Data Augmentation is a technique used during training, not a deployment strategy.