Model Inference and Deployment QUIZ (MCQ QUESTIONS AND ANSWERS)

To deploy the model in a new location

To ensure that only one version of the model is available

To release the new version to a small subset of users for testing

To increase the model's training time

A technique for making models faster

A process of updating model weights continuously

A phenomenon where the model's performance degrades over time due to changes in data distribution

A method for quantizing model parameters

It makes the model slower.

It simplifies model deployment.

It requires no internet connection.

It reduces model accuracy.

To increase the model's training time

To reduce the need for model optimization

To distribute model weights

To serve model predictions closer to end users, reducing latency

It improves training time.

It reduces model complexity.

It ensures that predictions are generated quickly.

It increases model size.

To check the health of the model's training data

To assess the physical health of the deployed model

To monitor the availability and functionality of deployed model instances

To optimize model hyperparameters

It simplifies model training.

It reduces the need for preprocessing.

It provides access to pre-trained models for various tasks.

It increases the complexity of the model.

To make the model slower

To improve model accuracy

To handle incoming requests and forward them to the appropriate model instance

To increase model complexity

Rolling back model training to a previous state

Rolling back model inference to a previous version

Reverting to a previous version of the deployed model

Rolling back model weights to their initial values

To train the model

To maintain model performance over time

To increase model complexity

To improve the model's training time

It requires extensive server management.

It allows for more control over hardware resources.

It scales automatically based on demand.

It increases latency.

Scalability

Cost

Model size

Data privacy

To make the model's code open source

To ensure that only one version of the model is deployed

To track and manage different versions of the model

To increase model complexity

To train the model continuously

To deploy the model only once

To automate the deployment pipeline and ensure consistent updates

To increase the model's training time

Quantization

Increasing the model size

Using larger batch sizes

Adding more layers to the model

To train the model

To fine-tune hyperparameters

To make predictions on new data

To validate the model

Model batch size

Model size

Model interpretability

Model preprocessing

To improve model accuracy

To distribute incoming requests evenly across multiple instances

To increase model complexity

To reduce latency

Quantization

Pruning

Increasing the model size

Using larger batch sizes

To train multiple models simultaneously

To compare the model's accuracy to a random baseline

To evaluate the model's performance in a real-world setting

To determine the model's training time

It makes the model faster.

It helps ensure model fairness.

It allows users to see the model's architecture.

It helps explain model predictions to users.

Model accuracy

Model interpretability

Model size

Model training time

The process of training a model

The process of deploying a model on edge devices

The process of making a trained model accessible for inference

The process of preprocessing data for training

The time it takes to train the model

The time it takes to preprocess data

The delay between sending a request and receiving a prediction

The size of the model's weights

To simplify model training

To allow real-time predictions over the internet

To make the model's code open source

To increase model complexity

Increasing the model's complexity

Using larger batch sizes during inference

Quantization and pruning of model parameters

Reducing the amount of data for inference

To make the model larger

To reduce the model's accuracy

To make the model more efficient and faster

To increase the number of model parameters

Deploying as a web service or API

Integrating into a mobile app

Printing the model's weights on paper

Running as a standalone software application

The process of creating a deep learning model

The process of training a deep learning model

The process of making a trained model available for use in applications

The process of fine-tuning model hyperparameters

Training involves data labeling, while deployment does not.

Training requires a GPU, while deployment can run on a CPU.

Training adjusts model weights, while deployment applies the trained model to new data.

Training uses a separate dataset, while deployment uses the training dataset.