PMLE - 5

What does the Vision API do?

• It only extracts the edges from an image by identifying the boundaries of objects within an image.

• It assigns labels to images and quickly classifies them into millions of predefined categories. It detects objects and faces, reads printed and handwritten text, and builds valuable metadata into the image catalog.

• The API identifies labels within a video instead of images.

• It compares the features of images, which may be different in orientation, perspective, lighting, size, and color.

What are the possible consequences for an ML model being trained with high resolution photos with high color depth? (Choose two answers)

• Performance issues such as insufficient computing power will not occur.

• It will increase the input size with longer training time for an ML model.

• It may lead to performance issues like insufficient computing power.

• It will increase the input size for an ML model but will reduce the training time.

What is true about batch prediction?

• Batch prediction is optimized to minimize the latency of serving predictions.

• Predictions returned in the response message.

• Batch prediction is useful for making several prediction requests at the same time and is optimized to handle a high volume of instances in a job.

• Batch prediction is a synchronous, or real-time, prediction, which means that it quickly returns a prediction.

When can a sequential model be used?

• A sequential model is appropriate for a plain stack of layers where each layer has multiple input tensors and one output tensor.

• A sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and multiple output tensors.

• A sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.

• A sequential model is appropriate for a plain stack of layers where each layer has multiple input tensors and multiple output tensors.

What kind of padding methods are available in Keras?

• Casual padding

• Same padding and casual padding

• Same padding, valid padding & Casual padding

• Casual padding and valid padding

What is data augmentation?

• Data augmentation is a set of techniques that enhance the size and quality of training datasets with the goal of creating more accurate ML models that generalize better.

• Data augmentation is the grouping together of resources for the purposes of maximizing advantage or minimizing risk to the users.

• Data augmentation is a technique where randomly selected neurons are ignored during training.

• Data augmentation is the amount of pixels added to an image when it is being processed by the kernel of a CNN.

What are the three major components that the Dialogflow API helps to identify in a conversation?

• Questions, answers, and feedback

• Time, location, and participants

• Intent (the topic), entity (the details), and context (the flow of the conversation).

• End-user, Dialogflow, and fulfillment