What Git command would you use to discard changes in a specific file before it has been staged?
- git checkout -- filename
- git reset HEAD filename
- git restore filename
- git revert filename
The correct option, git checkout -- filename, discards changes in a specific file before staging. It reverts the file to the last committed state. git reset HEAD filename unstages changes, git restore filename is used after staging, and git revert filename is for creating a new commit to undo changes.
A team is transitioning a large legacy codebase to Git. They encounter issues with large binary files. What Git feature should they consider using?
- Git LFS
- Git submodules
- Git cherry-pick
- Git rebase
Large binary files can be efficiently managed using Git LFS (Large File Storage). Git LFS is an extension that replaces large files in a repository with tiny pointer files while storing the actual file contents on a separate server. This helps in handling binary files more effectively.
A company is transitioning from SVN to Git. They want to ensure their historical branches and tags are preserved. What migration strategy should they use?
- Fast-Forward Merge
- Rebase
- Submodule
- git-svn
The git-svn option allows for a smooth transition from SVN to Git, preserving historical branches and tags. It maintains compatibility during migration.
If you want to predict whether an email is spam (1) or not spam (0), which regression technique would you use?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
For this classification task (spam or not spam), Logistic Regression is appropriate. It models the probability of the email being spam and maps it to a binary outcome.
An online retailer wants to recommend products to users. They have a vast inventory, and they're unsure which products are most likely to be purchased. Every time a product is recommended and purchased, the retailer gets a reward. This setup is reminiscent of which problem?
- Recommender Systems
- NLP for Sentiment Analysis
- Clustering and Dimensionality Reduction
- Reinforcement Learning
The retailer's challenge of recommending products and receiving rewards upon purchase aligns with Recommender Systems. In this problem, algorithms are used to predict user preferences and recommend items to maximize user satisfaction and sales.
In the Actor-Critic model, what role does the Critic's feedback play in adjusting the Actor's policies?
- Evaluating policy
- Selecting actions
- Providing rewards
- Discovering optimal actions
The Critic in the Actor-Critic model evaluates the current policy by estimating the value function. This evaluation helps the Actor make better decisions by guiding it towards actions that result in higher expected rewards, ultimately improving the policy.
In a DQN, the primary function of the neural network is to approximate which function?
- State-Action Value Function
- Policy Function
- Environment Dynamics Function
- Reward Function
The primary role of the neural network in a Deep Q Network (DQN) is to approximate the State-Action Value Function (Q-function).
In K-means clustering, the value of K represents the number of ________.
- Clusters
- Data Points
- Features
- Centroids
In K-means clustering, 'K' represents the number of clusters you want to partition your data into. Each cluster will have its centroid.
n the context of CNNs, why are pooling layers important despite them leading to a loss of information?
- Pooling layers help reduce the spatial dimensions, aiding in computation
- Pooling layers introduce non-linearity and increase model complexity
- Pooling layers reduce the number of filters in the network
- Pooling layers improve interpretability of features
Pooling layers are crucial for dimensionality reduction, making computations feasible, and for creating translation-invariant features. Despite information loss, it retains the most essential features.
What is the primary reason for using Random Forests over a single Decision Tree in many applications?
- Faster training time
- Increased accuracy
- Lower memory usage
- Simplicity
Random Forests are preferred due to their increased accuracy over single Decision Trees. They work by aggregating the predictions of multiple trees, which reduces overfitting and results in better overall performance.
In which learning approach does the model learn to...
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
In reinforcement learning, a model learns by interacting with an environment and receiving rewards or penalties based on its actions. It aims to make decisions to maximize cumulative rewards.
The value at which the sigmoid function outputs a 0.5 probability, thereby determining the decision boundary in logistic regression, is known as the ________.
- Decision Point
- Inflection Point
- Sigmoid Threshold
- Threshold Value
The value at which the sigmoid function outputs a 0.5 probability is known as the decision point. This is the threshold value that separates the two classes in a binary logistic regression.