Professional-Machine-Learning-Engineer Exam Dumps - Google Professional Machine Learning Engineer

 In this scenario, the goal is to predict the most relevant web banner that a user should see next on an online travel agency’s website. The model needs to have low latency requirements of 300ms@p99, and there are thousands of web banners to choose from. The exploratory analysis has shown that the navigation context is a good predictor. Security is also important to the company. Given these requirements, the best configuration for the prediction pipeline would be to embed the client on the website and deploy the model on AI Platform Prediction. Option A is the correct answer.

Option A: Embed the client on the website, and then deploy the model on AI Platform Prediction. This option is the simplest solution that meets the requirements. The client can collect the user’s navigation context and send it to the model deployed on AI Platform Prediction for prediction. AI Platform Prediction can handle large-scale prediction requests and has low latency requirements. This option does not require any additional infrastructure or services, making it the simplest solution.

Option B: Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction. This option adds an additional layer of infrastructure by deploying the gateway on App Engine. While App Engine can handle large-scale requests, it adds complexity to the pipeline and may not be necessary for this use case.

Option C: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction. This option adds even more complexity to the pipeline by deploying the database on Cloud Bigtable. While Cloud Bigtable can provide fast and scalable access to the user’s navigation context, it may not be needed for this use case. Moreover, Cloud Bigtable may introduce additional latency and cost to the pipeline.

Option D: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine. This option is the most complex and costly solution that does not meet the requirements. Deploying the model on Google Kubernetes Engine requires more management and configuration than AI Platform Prediction. Moreover, Google Kubernetes Engine may not be able to meet the low latency requirements of 300ms@p99. Deploying the database on Memorystore also adds unnecessary overhead and cost to the pipeline.

Cloud Vision API is a service that allows you to analyze images using pre-trained machine learning models 1 .  You can use Cloud Vision API to perform various tasks, such as face detection, text extraction, logo recognition, and object localization 1 .  Object localization is a feature that allows you to detect multiple objects in an image and draw bounding boxes around them 2 .  You can also get the labels and confidence scores for each detected object 2 .

By sending user-submitted images to the Cloud Vision API, you can use object localization to identify all objects in the image and compare the results against a list of animals.  You can use the  OBJECT_LOCALIZATION  feature type in the  A nnotateImageRequest  to request object localization 3 . You can then use the  localizedObjectAnnotations  field in the  AnnotateImageResponse  to get the list of detected objects, their labels, and their confidence scores. You can compare the labels with a predefined list of animals, such as dogs, cats, birds, etc., and determine whether the image has an animal or not.

This option is the best for your scenario, because it allows you to automatically and in near real time detect whether each uploaded photo has an animal, without requiring any manual labeling, model training, or model deployment. You can also prioritize time and minimize cost of your application development and deployment, as you can use the Cloud Vision API as a ready-to-use service, without needing any machine learning expertise or infrastructure.

The other options are not suitable for your scenario, because they either require manual labeling, model training, or model deployment, which would increase the time and cost of your application development and deployment, or they use object detection models, which are more complex and computationally expensive than object localization models, and are not necessary for your simple task of detecting whether an image has an animal or not.

The class imbalance problem is a common challenge in machine learning, especially in classification tasks. It occurs when the distribution of the target classes is highly skewed, such that one class (the majority class) has much more examples than the other class (the minority class). The minority class is often the more interesting or important class, such as failure incidents, fraud cases, or rare diseases. However, most machine learning algorithms are designed to optimize the overall accuracy, which can be biased towards the majority class and ignore the minority class. This can result in poor predictive performance, especially for the minority class.

There are different techniques to deal with the class imbalance problem, such as data-level methods, algorithm-level methods, and evaluation-level methods 1 . Data-level methods involve resampling the original dataset to create a more balanced class distribution. There are two main types of data-level methods: oversampling and undersampling. Oversampling methods increase the number of examples in the minority class, either by duplicating existing examples or by generating synthetic examples. Undersampling methods reduce the number of examples in the majority class, either by randomly removing examples or by using clustering or other criteria to select representative examples. Both oversampling and undersampling methods can be combined with upweighting or downweighting, which assign different weights to the examples according to their class frequency, to further balance the dataset.

For the use case of investigating failures of a production line component based on sensor readings, the best option is to downsample the data with upweighting to create a sample with 10% positive examples. This option involves randomly removing some of the negative examples (the majority class) until the ratio of positive to negative examples is 1:9, and then assigning higher weights to the positive examples to compensate for their low frequency. This option can create a more balanced dataset that can improve the performance of the classification models, while preserving the diversity and representativeness of the original data. This option can also reduce the computation time and memory usage, as the size of the dataset is reduced. Therefore, downsampling the data with upweighting to create a sample with 10% positive examples is the best option for this use case.

This answer is correct because it allows you to use the trained TensorFlow model for batch predictions on text data stored in BigQuery without any additional processing or overhead. Al Platform provides a service for running batch prediction jobs that can take input data from BigQuery or Cloud Storage and write the output to BigQuery or Cloud Storage. You can use the SavedModel format to export your TensorFlow model to Cloud Storage and then submit a batch prediction job that points to the model location and the input data location. Al Platform will handle the scaling and distribution of the prediction requests and return the results in the specified output location.  References:

[Al Platform: Batch prediction overview]

[Al Platform: Exporting a SavedModel for prediction]

Dataproc provides a managed Spark environment and integrates with Jupyter notebooks, ideal for large datasets and rapid prototyping. It reduces setup time compared to manual Spark configurations on Compute Engine or Vertex AI. Colab Enterprise is more suitable for small-scale prototyping rather than extensive Spark-based analysis.

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