A. Use BigQuery ML to run several regression models, and analyze their performance.

Answer DescriptionExplanation:

Option A is correct because using BigQuery ML to run several regression models, and analyze their performance is the most efficient and self-serviced way to complete the task. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries1. You can use BigQuery ML to run different types of regression models, such as linear regression, logistic regression, or DNN regression2. You can also use BigQuery ML to analyze the performance of your models, such as the mean squared error, the accuracy, or the ROC curve3. BigQuery ML is fast, scalable, and easy to use, as it does not require any data movement, coding, or additional tools4.

Option B is incorrect because reading the data from BigQuery using Dataproc, and running several models using SparkML is not the most efficient and self-serviced way to complete the task. Dataproc is a service that allows you to create and manage clusters of virtual machines that run Apache Spark and other open-source tools5. SparkML is a library that provides ML algorithms and utilities for Spark. However, this option requires more effort and resources than option A, as it involves moving the data from BigQuery to Dataproc, creating and configuring the clusters, writing and running the SparkML code, and analyzing the results.

Option C is incorrect because using Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics is not the most efficient and self-serviced way to complete the task. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. Scikit-learn is a library that provides ML algorithms and utilities for Python. However, this option also requires more effort and resources than option A, as it involves creating and managing the notebooks, writing and running the scikit-learn code, and analyzing the results.

Option D is incorrect because training a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms is not the most efficient and self-serviced way to complete the task. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI is a service that allows you to train and deploy ML models using built-in algorithms or custom containers. However, this option also requires more effort and resources than option A, as it involves writing and running the TensorFlow code, creating and managing the training jobs, and analyzing the results.

References:

BigQuery ML overview
Creating a model in BigQuery ML
Evaluating a model in BigQuery ML
BigQuery ML benefits
Dataproc overview
[SparkML overview]
[Vertex AI Workbench overview]
[Scikit-learn overview]
[TensorFlow overview]
[Vertex AI overview]

D. Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

Answer DescriptionExplanation:

According to the web search results, TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines using TensorFlow1. TFX provides a set of components that can be orchestrated using either the TFX SDK or Kubeflow Pipelines. TFX components can handle different aspects of the pipeline, such as data ingestion, data validation, data transformation, model training, model evaluation, model serving, and more. TFX components can also leverage other Google Cloud services, such as Dataflow2 and Vertex AI3. Dataflow is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Vertex AI is a unified platform for machine learning development and deployment.

Vertex AI offers various services and tools for building, managing, and serving machine learning models. Therefore, option D is the best way to create a low maintenance, automated workflow for the given use case, as it allows you to use the TFX SDK to define and execute your pipeline components, and use Dataflow and Vertex AI services to scale and optimize your pipeline. The other options are not relevant or optimal for this scenario.

References:

TensorFlow Extended
Dataflow
Vertex AI
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

A. The model is overfitting in areas with less traffic and underfitting in areas with more traffic.

Answer DescriptionExplanation:

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

C. User engagement as measured by the number of battles played daily per user

Answer DescriptionExplanation:

The best business metric to track to measure the model’s performance is user engagement as measured by the number of battles played daily per user. This metric reflects the main goal of the model, which is to enhance the user experience and satisfaction by creating balanced and fair battles. If the model is successful, it should increase the user retention and loyalty, as well as the word-of-mouth and referrals. This metric is also easy to measure and interpret, as it can be directly obtained from the user activity data.

The other options are not optimal for the following reasons:

A. Average time players wait before being assigned to a team is not a good metric, as it does not capture the quality or outcome of the battles. It only measures the efficiency of the model, which is not the primary objective. Moreover, this metric can be influenced by external factors, such as the availability and demand of players, the network latency, and the server capacity.

B. Precision and recall of assigning players to teams based on their predicted versus actual ability is not a good metric, as it is difficult to measure and interpret. It requires having a reliable and consistent way of estimating the player’s ability, which can be subjective and dynamic. It also requires having a ground truth label for each assignment, which can be costly and impractical to obtain. Moreover, this metric does not reflect the user feedback or satisfaction, which is the ultimate goal of the model.

D. Rate of return as measured by additional revenue generated minus the cost of developing a new model is not a good metric, as it is not directly related to the model’s performance. It measures the profitability of the model, which is a secondary objective. Moreover, this metric can be affected by many other factors, such as the market conditions, the pricing strategy, the marketing campaigns, and the competition.

References:

Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Google Cloud launches machine learning engineer certification
How to measure user engagement
How to choose the right metrics for your machine learning model

C. 1 Use TFX components with Dataflow to encode the text features and scale the numerical features.
2 Export results to Cloud Storage as TFRecords.
3 Feed the data into Vertex Al Training.

Answer DescriptionExplanation:

TFX (TensorFlow Extended) is a platform for end-to-end machine learning pipelines. It provides components for data ingestion, preprocessing, validation, model training, serving, and monitoring. Dataflow is a fully managed service for scalable data processing. By using TFX components with Dataflow, you can perform feature engineering on large-scale tabular data in a distributed and efficient way. You can use the Transform component to apply the MaxMin scaler and the one-hot encoding to the numerical and categorical features, respectively. You can also use the ExampleGen component to read data from BigQuery and the Trainer component to train your TensorFlow model. The output of the Transform component is a TFRecord file, which is a binary format for storing TensorFlow data. You can export the TFRecord file to Cloud Storage and feed it into Vertex AI Training, which is a managed service for training custom machine learning models on Google Cloud.

References:

TFX | TensorFlow
Dataflow | Google Cloud
Vertex AI Training | Google Cloud

D. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.

Answer DescriptionExplanation:

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

B. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring

Answer DescriptionExplanation:

Option A is incorrect because Vertex AI Pipelines and App Engine do not meet all the requirements of the system. Vertex AI Pipelines is a service that allows you to create, run, and manage ML workflows using TensorFlow Extended (TFX) components or custom components1. App Engine is a service that allows you to build and deploy scalable web applications using standard or flexible environments2. However, App Engine does not support Docker containers in the standard environment, and does not provide a dedicated service for online prediction and monitoring of ML models3.

Option B is correct because Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring meet all the requirements of the system. Vertex AI Prediction is a service that allows you to deploy and serve ML models for online or batch prediction, with support for autoscaling and custom containers4. Vertex AI Model Monitoring is a service that allows you to monitor the performance and fairness of your deployed models, and get alerts for any issues or anomalies5.

Option C is incorrect because Cloud Composer, BigQuery ML, and Vertex AI Prediction do not meet all the requirements of the system. Cloud Composer is a service that allows you to create, schedule, and manage workflows using Apache Airflow. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries. However, BigQuery ML does not support custom containers, and Vertex AI Prediction does not support scheduled model retraining or model monitoring.

Option D is incorrect because Cloud Composer, Vertex AI Training with custom containers, and App Engine do not meet all the requirements of the system. Vertex AI Training is a service that allows you to train ML models using built-in algorithms or custom containers. However, Vertex AI Training does not support online prediction or model monitoring, and App Engine does not support Docker containers in the standard environment or online prediction and monitoring of ML models3.

References:

Vertex AI Pipelines overview
App Engine overview
Choosing an App Engine environment
Vertex AI Prediction overview
Vertex AI Model Monitoring overview
[Cloud Composer overview]
[BigQuery ML overview]
[BigQuery ML limitations]
[Vertex AI Training overview]

B. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

Answer DescriptionExplanation:

This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons:

It indicates that the model is not learning from the data, but rather memorizing the majority class. Since 90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the model’s accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription).

It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the model’s accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the model’s accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them.

References:

How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery
Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery