Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

Quantization is a technique that reduces the numerical precision of the weights and activations of a neural network, which can improve the inference speed and reduce the memory footprint of the model1.

Reducing the floating point precision from tf.float64 to tf.float16 can potentially halve the latency and memory usage of the model, while having minimal impact on the accuracy2.

Increasing the dropout rate to 0.8 in either mode would not affect the latency, but would likely degrade the performance of the model significantly, as dropout is a regularization technique that randomly drops out units during training to prevent overfitting3.

Switching from CPU to GPU serving may or may not improve the latency, depending on the hardware specifications and the model complexity, but it would also incur additional costs and complexity for deployment4

The number of trainable weights in a DNN regression model with Keras APIs can be calculated by multiplying the number of input units by the number of output units for each layer, and adding the number of bias units for each layer. The bias units are usually equal to the number of output units, except for the last layer, which does not have bias units if the activation function is softmax1. In this code, the model has three layers: a dense layer with 256 units and relu activation, a dropout layer with 0.25 rate, and a dense layer with 2 units and softmax activation. The input shape is 500. Therefore, the number of trainable weights is:

For the first layer: 500 input units * 256 output units + 256 bias units = 128256

For the second layer: The dropout layer does not have any trainable weights, as it only randomly sets some of the input units to zero to prevent overfitting2.

For the third layer: 256 input units * 2 output units + 0 bias units = 512

The total number of trainable weights is 128256 + 512 = 161024. Therefore, the correct answer is B.

References:

How to calculate the number of parameters for a Convolutional Neural Network?

Dropout (keras.io)

 Vertex AI Experiments is a service that allows you to track, compare, and manage experiments with Vertex AI. You can use Vertex AI Experiments to record the parameters, metrics, and artifacts of each model training run, and compare them in a graphical interface. Vertex AI Experiments supports models trained in Vertex AI Pipelines, Vertex AI Custom Training, and Vertex AI Workbench notebooks. To use Vertex AI Experiments, you need to create an experiment and submit your pipeline runs or custom training jobs as experiment runs. For models trained on notebooks, you need to use the Vertex AI SDK to log the parameters and metrics to the experiment. This way, you can minimize the effort required to store and compare the model performance across different services. References: Track, compare, manage experiments with Vertex AI Experiments, Vertex AI Pipelines: Metrics visualization and run comparison using the KFP SDK, [Vertex AI SDK for Python]