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

The best option for reducing the sensitivity of the dataset before training the model is to use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption. This option allows you to keep every column in the dataset, while protecting the sensitive data from unauthorized access or exposure.  The Cloud DLP API can detect and classify various types of sensitive data, such as names, email addresses, phone numbers, credit card numbers, and more 1 .  Dataflow can create scalable and reliable pipelines to process large volumes of data from BigQuery and other sources 2 .  Format Preserving Encryption (FPE) is a technique that encrypts sensitive data while preserving its original format and length, which can help maintain the utility and validity of the data 3 . By using Dataflow with the DLP API, you can apply FPE to the sensitive values in the dataset, and store the encrypted data in BigQuery or another destination.  Yo u can also use the same pipeline to decrypt the data when needed, by using the same encryption key and method 4 .

The other options are not as suitable as option B, for the following reasons:

Option A: Using Dataflow to ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column, would reduce the sensitivity of the data, but also the utility and accuracy of the data.  Randomization is a technique that replaces sensitive data with random values, which can prevent re-identification of t he data, but also distort the distribution and relationships of the data 3 . This can affect the performance and quality of the ML model, especially if every column is critical to the model.

Option C: Using the Cloud DLP API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt, would reduce the sensitivity of the data, but also the utility and validity of the data. AES-256 is a symmetric encryption algorithm that uses a 256-bit key to encrypt and decrypt data. A salt is a random value that is added to the data before encryption, to increase the randomness and security of the encrypted data. However, AES-256 does not preserve the format or length of the original data, which can cause problems when storing or processing the data.  For example, if the original data is a 10-digit phone number, AES-256 would produce a much longer and different string, which can break the schema or logic of the dataset 3 .

Option D: Before training, using BigQuery to select only the columns that do not contain sensitive data, and creating an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals, would reduce the exposure of the sensitive data, but also the completeness and relevance of the data. An authorized view is a BigQuery view that allows you to share query results with particular users or groups, without giving them access to the underlying tables. However, this option assumes that you can identify the columns that do not contain sensitive data, which may not be easy or accurate. Moreover, this option would remove some columns from the dataset, which can affect the performance and quality of the ML model, especially if every column is critical to the model.

The most likely reason for the observed result is that the model is overfitting in areas with less traffic and underfitting in areas with more traffic. Overfitting means that the model learns the specific patterns and noise in the training data, but fails to generalize well to new and unseen data. Underfitting means that the model is not able to capture the complexity and variability of the data, and performs poorly on both training and test data. In this case, the model might have learned to segment the images well when there is less traffic, but it might not have enough data or features to handle the more challenging scenarios when there is more traffic. This could lead to a decrease in the AUC metric, which measures the ability of the model to distinguish between different classes. AUC is a suitable metric for this classification model, as it is not affected by class imbalance or threshold selection. The other options are not likely to be the reason for the result, as they are not related to the traffic density. Too much data representing congested areas would not cause the model to fail in those areas, but rather help the model learn better. Gradients vanishing or exploding is a problem that occurs during the training process, not after the deployment, and it affects the whole model, not specific scenarios.  References :

Image Segmentation: U-Net For Self Driving Cars

Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Sharing Pixelopolis, a self-driving car demo from Google I/O built with TensorFlow Lite

Google Cloud launches machine learning engineer certification

Google Professional Machine Learning Engineer Certification

Professional ML Engineer Exam Guide

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

AutoML Translation is a service that allows you to create and train custom ML models for translating text between different languages. You can use AutoML Translation to train a model that can translate instruction manuals for scientific products to 15 different languages. You can also use Translation Hub to configure a project and use the trained model to translate the documents. Translation Hub is a service that allows you to manage and automate your translation workflows on Google Cloud. You can use Translation Hub to upload the documents to a Cloud Storage bucket, select the source and target languages, and apply the trained model to translate the documents. You can also use Translation Hub to download the translated documents or save them to another Cloud Storage bucket. You can also use human reviewers to evaluate the incorrect translations. Human reviewers are people who can review and correct the translations produced by the ML model. You can use human reviewers to improve the quality and accuracy of the translations, and provide feedback to the ML model. You can use Translation Hub to integrate with third-party human review services, such as Google Translate Community or Appen. By using AutoML Translation, Translation Hub, and human reviewers, you can implement a scalable solution that maximizes accuracy and minimizes operational overhead. You can also include a process to evaluate and fix incorrect translations.  References :

[AutoML Translation documentation]

[Translation Hub documentation]

[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]