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

The most likely problem is that the tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table. This is because the RAND() function generates a random number between 0 and 1 for each row, and the probability of a row being in both the training and validation tables is 0.2 * 0.8 = 0.16, which is not negligible. This means that some of the records that you use to validate your model are also used to train your model, which can lead to overfitting and poor generalization. Moreover, the probability of a row being in neither the training nor the validation table is 0.2 * 0.2 = 0.04, which means that you are wasting some of the data in your initial table and reducing the size of your datasets. A better way to split your data into training and validation sets is to use a hash function on a unique identifier column, such as the following queries:

CREATE OR REPLACE TABLE ‘myproject.mydataset.training‘ AS (SELECT * FROM ‘myproject.mydataset.mytable‘ WHERE MOD(FARM_FINGERPRINT(id), 10) < 8); CREATE OR REPLACE TABLE ‘myproject.mydataset.validation‘ AS (SELECT * FROM ‘myproject.mydataset.mytable‘ WHERE MOD(FARM_FINGERPRINT(id), 10) > = 8);

This way, you can ensure that each row has a fixed 80% chance of being in the training table and a 20% chance of being in the validation table, without any overlap or omission.

AI Platform Training is a service that allows you to run your machine learning experiments on Google Cloud using various features, model architectures, and hyperparameters.  You can use AI Platform Training to scale up your experiments, leverage distributed training, and access specialized hardware such as GPUs and TPUs 1 . Cloud Monitoring is a service that collects and analyzes metrics, logs, and traces from Google Cloud, AWS, and other sources.  You can use Cloud Monitoring to create dashboards, alerts, and reports based on your data 2 .  The Monitoring API is an interface that allows you to programmatically access and manipulate your monitoring data 3 .

By using AI Platform Training and Cloud Monitoring, you can track and report your experiments while minimizing manual effort.  You can write the accuracy metrics from your experiments to Cloud Monitoring usin g the AI Platform Training Python package 4 . You can then query the results using the Monitoring API and compare the performance of different experiments.  You can also visualize the metrics in the Cloud Console or create custom dashboards and alerts 5 . Therefore, using AI Platform Training and Cloud Monitoring is the best option for this use case.

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 softmax 1 . 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 overfitting 2 .

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.