Estimating the price of a house is an example of a regression application, as it involves predicting a continuous value.

Predicting house prices based on features is a regression problem, as it involves predicting a continuous value.

Predicting the temperature for tomorrow is a regression task as it involves predicting a continuous value.

A syntax error occurs when the code does not conform to the grammatical rules of the programming language, such as missing a semicolon.

Google Drive is an example of cloud storage.

K-Means Clustering is an unsupervised learning technique that identifies patterns in data without labeled outcomes.

Customer segmentation is an example of unsupervised learning, where the model identifies patterns in data without labeled outcomes.

Clustering similar data points is an example of unsupervised learning, where the model identifies patterns without labeled outcomes.

Market basket analysis is an example of unsupervised learning, where patterns are discovered without labeled outcomes.

Increased code complexity is NOT a benefit of code optimization; optimizations aim to maintain or reduce complexity.

Effective feature engineering does not eliminate the need for data preprocessing; it often requires it to prepare the data for modeling.

Increased data redundancy is not a benefit of feature engineering; rather, effective feature engineering aims to reduce redundancy and improve the quality of the features used.

Dijkstra's algorithm is not commonly applied in social network analysis, as it is primarily used for shortest path problems in weighted graphs.

Model training time is not a challenge in deployment; rather, it is a concern during the model development phase.

A binary search tree does not require all nodes to have two children; it can have zero, one, or two children.

In a binary search tree, it is not necessary for all nodes to have two children; some nodes can have one or no children.

In a binary tree, not all nodes have to have the same number of children; some can have zero, one, or two children.

Linked lists do not support random access; elements must be accessed sequentially.

Stacks operate in a Last In First Out (LIFO) manner and do not allow random access; elements can only be accessed in the order they were added.

Arrays have a fixed size and do not support dynamic resizing; this is a characteristic of linked lists.

AVL trees can have multiple children; they are binary trees, and the characteristic that they can have at most one child is incorrect.

BFS is typically implemented using a queue, not recursion, which is a characteristic of DFS.

Binary search does not work on unsorted data; it requires the data to be sorted.

High upfront costs are not a characteristic of cloud ML services, which typically operate on a pay-as-you-go model.

DFS does not guarantee the shortest path; it explores as far as possible along each branch before backtracking.

DFS explores as far as possible along each branch before backtracking, which means it does not explore all neighbors before going deeper.

Dijkstra's algorithm cannot be used for graphs with negative weights, which is a key characteristic.

The greedy choice property is not a characteristic of dynamic programming; it is associated with greedy algorithms, which make local optimal choices.

Greedy algorithms do not guarantee an optimal solution for all problems; they work well for specific types of problems.

Hierarchical clustering does not require the number of clusters to be specified in advance; it builds a hierarchy of clusters.