 BigQuery ML allows you to build and run machine learning models using SQL queries directly within BigQuery, which is one of the simplest approaches because it doesn ' t require setting up an external environment like Vertex AI or managing infrastructure.

 AutoML regression is more appropriate for predicting customer lifetime value (CLV) compared to ARIMA, which is typically used for time series forecasting (e.g., sales over time, stock prices, etc.). CLV prediction involves understanding complex relationships between customer behavior and value, which is best captured by a regression model.

 Using BigQuery Studio and running a CREATE MODEL statement to build an AutoML regression model offers the simplicity you ' re looking for because it automates much of the feature engineering, model selection, and hyperparameter tuning.

 The other options involving ARIMA models (A and B) are not appropriate for CLV, and setting up a Vertex AI Workbench notebook (D) introduces unnecessary complexity for this task.

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?

A. Import the TensorFlow model by using the create model statement in BigQuery ML. Apply the historical data to the TensorFlow model.

B. Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

C. Export the historical data to Cloud Storage in CSV format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

D. Configure and deploy a Vertex Al endpoint. Use the endpoint to get predictions from the historical data in BigQuery.

Answer: B

 Vertex AI batch prediction is the most appropriate and efficient way to apply a pre-trained model like TensorFlow’s SavedModel to a large dataset, especially for batch processing.

 The Vertex AI batch prediction job works by exporting your dataset (in this case, historical data from BigQuery) to a suitable format (like Avro or CSV ) and then processing it in Cloud Storage where the model is stored.

 Avro format is recommended for large datasets as it is highly efficient for data storage and is optimized for read/write operations in Google Cloud, which is why option B is correct.

 Option A suggests using BigQuery ML for inference, but it does not support running arbitrary TensorFlow models directly within BigQuery ML. Hence, BigQuery ML is not a valid option for this particular task.

 Option C (exporting to CSV) is a valid alternative but is less efficient compared to Avro in terms of performance.

 Option D suggests deploying a Vertex AI endpoint, which is better suited for real-time inference rather than batch inference. Since the question asks for batch inference, B is the best answer.

The best option for deploying a custom tabular ML model to production for online predictions, and monitoring the feature distribution over time with minimal effort, using a model that was provided by the bank’s vendor, the training data is not available due to its sensitivity, and the model is packaged as a Vertex AI Model serving container which accepts a string as input for each prediction instance, is to upload the model to Vertex AI Model Registry and deploy the model to a Vertex AI endpoint, create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema. This option allows you to leverage the power and simplicity of Vertex AI to serve and monitor your model with minimal code and configuration. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained model to an online prediction endpoint, which can provide low-latency predictions for individual instances. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A Vertex AI Model Registry is a resource that can store and manage your models on Vertex AI. A Vertex AI Model Registry can help you organize and track your models, and access various model information, such as model name, model description, and model labels. A Vertex AI Model serving container is a resource that can run your custom model code on Vertex AI. A Vertex AI Model serving container can help you package your model code and dependencies into a container image, and deploy the container image to an online prediction endpoint. A Vertex AI Model serving container can accept various input formats, such as JSON, CSV, or TFRecord. A string input format is a type of input format that accepts a string as input for each prediction instance. A string input format can help you encode your feature values into a single string, and separate them by commas. By uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, you can serve your model for online predictions with minimal code and configuration. You can use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, and provide the model name, model description, and model labels. You can also use the Vertex AI API or the gcloud command-line tool to deploy the model to a Vertex AI endpoint, and provide the endpoint name, endpoint description, endpoint labels, and endpoint resources. A Vertex AI Model Monitoring job is a resource that can monitor the performance and quality of your deployed models on Vertex AI. A Vertex AI Model Monitoring job can help you detect and diagnose issues with your models, such as data drift, prediction drift, training/serving skew, or model staleness. Feature drift is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model over time. Feature drift can indicate that the online data is changing over time, and the model performance is degrading. By creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, you can monitor the feature distribution over time with minimal effort. You can use the Vertex AI API or the gcloud command-line tool to create a Vertex AI Model Monitoring job, and provide the monitoring objective, the monitoring frequency, the alerting threshold, and the notification channel. You can also provide an instance schema, which is a JSON file that describes the features and their types in the prediction input data.  An instance schema can he lp Vertex AI Model Monitoring parse and analyze the string input format, and calculate the feature distributions and distance scores 1 .

The other options are not as good as option A, for the following reasons:

Option B: Uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema would not help you monitor the changes in the online data over time, and could cause errors or poor performance. Feature skew is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model at a given point in time. Feature skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema, you can monitor the feature distribution at a given point in time with minimal effort. However, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema would not help you monitor the changes in the online data over time, and could cause errors or poor performance. You would need to use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, create a Vertex AI Model Monitoring job, and provide an instance schema.  Moreover, this option would not monitor the feature drift, which is a more direct and relevant metric for measuring the changes in the online data over time, and the model pe rformance and quality 1 .

Option C: Refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema. A key-value pair input format is a type of input format that accepts a key-value pair as input for each prediction instance. A key-value pair input format can help you specify the feature names and values in a JSON object, and separate them by colons. By refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, you can serve and monitor your model with minimal code and configuration. You can write code to refactor the serving container to accept key-value pairs as input format, and use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job. However, refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema. You would need to write code, refactor the serving container, upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job.  Moreover, this option would not use the instance schema, which is a JSON file that can help Vertex AI Model Monitoring parse and analyze the string input format, and calcula te the feature distributions and distance scores 1 .

Option D: Refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, and would not help you monitor the changes in the online data over time, and could cause errors or poor performance. Feature skew is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model at a given point in time. Feature skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, you can monitor the feature distribution at a given point in time with minimal effort. However, refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, and would not help you monitor the changes in the online data over time, and could cause errors or poor performance. You would need to write code, refactor the serving container, upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job.  Moreover, this option woul d not monitor the feature drift, which is a more direct and relevant metric for measuring the changes in the online data over time, and the model performance and quality 1 .

Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to store features such as parcels delivered and truck locations over time, and retrieve them with low latency for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can also use Vertex AI Feature Store to retrieve historical data at a specific point in time for model training. Model training is a process of learning the parameters of a ML model from data. By using Vertex AI Feature Store, you can store the features with minimal effort, and avoid the complexity of managing your own data storage and serving system.  References :

Vertex AI Feature Store documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

The keywords here are " quickly prototype " and " minimize time and cost. "

Natural Language API: Google’s pre-trained Natural Language API provides out-of-the-box sentiment analysis. It requires zero training time, zero infrastructure management, and no labeling effort. This is the fastest and most cost-effective way to get a baseline/prototype.

Why other options are incorrect:

Option A: Fine-tuning a BERT model is computationally expensive and requires significant engineering time.

Option C: AutoML is a great secondary step if the API is not accurate enough, but it still requires time to train and evaluate, making it slower than the pre-trained API.

Option D: Vectorizing text and training a regression model is unnecessarily complex for a standard sentiment classification task.

Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user’s past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading.  Collaborative filtering is a method of recommendation that uses the ratings or prefe rences of other users to predict the preferences of a target user 1 . However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.

Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading.  Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus o f text, such that words that are semantically similar have similar vectors 2 . By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage.  Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article 3 .

Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading.  Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user prof ile or article content) 4 . However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user’s preference.

Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they are currently reading.  SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (such as news categories) with the maximum margin 5 . However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.

The training time of a machine learning model depends on several factors, such as the complexity of the model, the size of the data, the hardware resources, and the hyperparameters. To minimize the training time without significantly compromising the accuracy of the model, one should optimize these factors as much as possible.

One of the factors that can have a significant impact on the training time is the scale-tier parameter, which specifies the type and number of machines to use for the training job on AI Platform.  The scale-tier parameter can be one of th e predefined values, such as BASIC, STANDARD_1, PREMIUM_1, or BASIC_GPU, or a custom value that allows you to configure the machine type, the number of workers, and the number of parameter servers 1

To speed up the training of an LSTM-based model on AI Platform, one should modify the scale-tier parameter to use a higher tier or a custom configuration that provides more computational resources, such as more CPUs, GPUs, or TPUs. This can reduce the training time by increasing the paral lelism and throughput of the model training.  However, one should also consid er the trade-off between the training time and the cost, as higher tiers or custom configurations may incur higher charges 2

The other options are not as effective or may have adverse effects on the model accuracy. Modifying the epochs parameter, which specifies the number of times the model sees the entire dataset, may reduce the training time, but also affect the model’s convergence and performance. Modifying the batch size parameter, which specifies the number of examples per batch, may affect the model’s stability and generalization ability, as well as the memory usage and the gradient update frequency.  Modifying the learning rate parame ter, which specifies the step size of the gradient descent optimization, may affect the model’s convergence and performance, as well as the risk of overshooting or getting stuck in local minima 3

According to the official exam guide 1 , one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”.  AutoML Vision 2  is a service that allows you to train and deploy custom vision models for image classification and object detection. AutoML Vision simplifies the model development process by providing a graphical user interface and a no-code approach.  You can use AutoML Vision to train an object detection model by using the annotated image data, and evaluate the model performance using metrics such as mean average precision (mAP) and intersection over union (IoU) 3 . Therefore, option B is the best way to quickly create an initial model for the given use case. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

AutoML Vision

Obj ect detection evaluation

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

According to the official exam guide 1 , one of the skills assessed in the exam is to “explain the predictions of a trained model”.  Vertex AI provides feature attributions using Shapley Values, a cooperative game theory algorithm that assigns credit to each feature in a model for a particular outcome 2 . Feature attributions can help you understand how the model calculates the predictions and debug or optimize the model accordingly.  You can use Forecasting with AutoML or Tabular Workflow for Forecasting to generate and query local feature attributions 2 . The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Feature attributions for forecasting

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

 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.