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 The best option for configuring the workflow is to add the transformations as a preprocessing layer in the TensorFlow models. This option allows you to leverage the power and simplicity of TensorFlow to preprocess and transform the data with simple Python code. TensorFlow is a framework for building and training machine learning models. TensorFlow provides various tools and libraries for data analysis and machine learning. A preprocessing layer is a type of layer in TensorFlow that can perform data preprocessing and feature engineering operations on the input data. A preprocessing layer can help you customize the data transformation and preprocessing logic, and handle complex or non-standard data formats. A preprocessing layer can also help you minimize the preprocessing time, cost, and development effort, as you only need to write a few lines of code to implement the preprocessing layer, and you do not need to create any intermediate data sources or pipelines.  By adding the transformations as a preprocessing layer in the TensorFlow models, you can use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales 1 .

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

Option A: Writing the transformations into Spark that uses the spark-bigquery-connector and using Dataproc to preprocess the data would require more skills and steps than using a preprocessing layer in TensorFlow. Spark is a framework for distributed data processing and machine learning. Spark can read and write data from BigQuery by using the spark-bigquery-connector, which is a library that allows Spark to communicate with BigQuery. Dataproc is a service that can create and manage Spark clusters on Google Cloud. Dataproc can help you run Spark jobs on Google Cloud, and scale the clusters according to the workload. However, writing the transformations into Spark that uses the spark-bigquery-connector and using Dataproc to preprocess the data would require more skills and steps than using a prepro cessing layer in TensorFlow. You would need to write code, create and configure the Spark cluster, install and import the spark-bigquery-connector, load and preprocess the data, and write the data back to BigQuery.  Moreover, this option would create an intermediate data source in BigQuery, wh ich can increase the storage and computation costs 2 .

Option B: Writing SQL queries to transform the data in-place in BigQuery would not allow you to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. BigQuery is a service that can perform data analysis and machine learning by using SQL queries. BigQuery can perform data transformation and preprocessing by using SQL functions and clauses, such as MIN, MAX, CASE, and TRANSFORM. BigQuery can also perform machine learning by using BigQuery ML, which is a feature that can create and train machine learning models by using SQL queries. However, writing SQL queries to transform the data in-place in BigQuery would not allow you to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. Vertex AI’s custom training service is a service that can run your custom machine learning code on Vertex AI. Vertex AI’s custom training service can support various machine learning frameworks, such as TensorFlow, PyTorch, and scikit-learn. Vertex AI’s custom training service cannot support SQL queries, as SQL is not a machine learning framework.  Therefore, if you want to use Vertex AI’s custom training service, you cannot use SQL queries to transfor m the data in-place in BigQuery 3 .

Option D: Creating a Dataflow pipeline that uses the BigQueryIO connector to ingest the data, process it, and write it back to BigQuery would require more skills and steps than using a preprocessing layer in TensorFlow. Dataflow is a service that can create and run data processing and machine learning pipelines on Google Cloud. Dataflow can read and write data from BigQuery by using the BigQueryIO connector, which is a library that allows Dataflow to communicate with BigQuery. Dataflow can perform data transformation and preprocessing by using Apache Beam, which is a framework for distributed data processing and machine learning. However, creating a Dataflow pipeline that uses the BigQueryIO connector to ingest the data, process it, and write it back to BigQuery would require more skills and steps than using a preprocessing layer in TensorFlow. You would need to write code, create and configure the Dataflow pipeline, install and import the BigQueryIO connector, load and preprocess the data, and write the data back to BigQuery.  Moreover, this o ption would create an intermediate data source in BigQuery, which can increase the storage and computation costs 4 .

The best hardware to choose for your models is a cluster with 2 a2-megagpu-16g machines, each with 16 NVIDIA Tesla A100 GPUs (640 GB GPU memory in total), 96 vCPUs, and 1.4 TB RAM. This hardware configuration can provide you with enough compute power, memory, and bandwidth to handle your large and complex deep learning models, as well as your custom TensorFlow ops in C++. The NVIDIA Tesla A100 GPUs are the latest and most advanced GPUs from NVIDIA, which offer high performance, scalability, and efficiency for various ML workloads. They also support multi-instance GPU (MIG) technology, which allows you to partition each GPU into up to seven smaller instances, each with its own memory, cache, and compute cores. This can enable you to run multiple experiments in parallel, or to optimize the resource utilization and cost efficiency of your models. The a2-megagpu-16g machines are part of the Google Cloud Accelerator-Optimized VM (A2) family, which are designed to provide the best performance and flexibility for GPU-intensive applications. They also offer high-speed NVLink interconnects between the GPUs, which can improve the data transfer and communication between the GPUs. Moreover, the a2-megagpu-16g machines have 96 vCPUs and 1.4 TB RAM, which can support the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.

The other options are not optimal for the following reasons:

A. A cluster with 2 n1-highcpu-64 machines, each with 8 NVIDIA Tesla V100 GPUs (128 GB GPU memory in total), and a n1-highcpu-64 machine with 64 vCPUs and 58 GB RAM is not a good option, as it has less GPU memory, compute power, and bandwidth than the a2-megagpu-16g machines. The NVIDIA Tesla V100 GPUs are the previous generation of GPUs from NVIDIA, which have lower performance, scalability, and efficiency than the NVIDIA Tesla A100 GPUs. They also do not support the MIG technology, which can limit the flexibility and optimization of your models. Moreover, the n1-highcpu-64 machines are part of the Google Cloud N1 VM family, which are general-purpose VMs that do not offer the best performance and features for GPU-intensive applications. They also have lower vCPUs and RAM than the a2-megagpu-16g machines, which can affect the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.

C. A cluster with an n1-highcpu-64 machine with a v2-8 TPU and 64 GB RAM is not a good option, as it has less GPU memory, compute power, and bandwidth than the a2-megagpu-16g machines. The v2-8 TPU is a cloud tensor processing unit (TPU) device, which is a custom ASIC chip designed by Google to accelerate ML workloads. However, the v2-8 TPU is the second generation of TPUs, which have lower performance, scalability, and efficiency than the latest v3-8 TPUs. They also have less memory and bandwidth than the NVIDIA Tesla A100 GPUs, which can limit the size and complexity of your models, as well as the data transfer and communication between the devices. Moreover, the n1-highcpu-64 machine has lower vCPUs and RAM than the a2-megagpu-16g machines, which can affect the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.

D. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM is not a good option, as it does not have any GPUs, which are essential for accelerating deep learning models. The n1-highcpu-96 machines are part of the Google Cloud N1 VM family, which are general-purpose VMs that do not offer the best performance and features for GPU-intensive applications. They also have lower RAM than the a2-megagpu-16g machines, which can affect the memory requirements of your models, as well as the data preprocessing and postprocessing tasks.

Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters.  You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs 1 . An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups.  You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard 2 .

For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.

 The best option for developing an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket is to import the labeled images as a managed dataset in Vertex AI and use AutoML to train the model. This option allows you to leverage the power and simplicity of Google Cloud to create and deploy a high-quality image classification 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 create a managed dataset from a Cloud Storage bucket that contains labeled images, which can be used to train an AutoML model. AutoML is a service that can automatically build and optimize machine learning models for various tasks, such as image classification, object detection, natural language processing, and tabular data analysis. AutoML can handle the complex aspects of machine learning, such as feature engineering, model architecture, hyperparameter tuning, and model evaluation. AutoML can also evaluate, deploy, and monitor the image classification model, and provide online or batch predictions. By using Vertex AI and AutoML, users can develop an image classification model by using a large dataset with ease and efficiency.

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

Option A: Using Vertex AI Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model would require more skills and steps than using Vertex AI and AutoML. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. Kubeflow Pipelines SDK is a Python library that can create and run pipelines on Vertex AI Pipelines or on Kubeflow, an open-source platform for machine learning on Kubernetes. However, using Vertex AI Pipelines and Kubeflow Pipelines SDK would require writing code, building Docker images, defining pipeline components and steps, and managing the pipeline execution and artifacts. Moreover, Vertex AI Pipelines and Kubeflow Pipelines SDK are not specialized for image classification, and users would need to use other libraries or frameworks, such as TensorFlow or PyTorch, to build and train the image classification model.

Option B: Using Vertex AI Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trains the model would require more skills and steps than using Vertex AI and AutoML. TensorFlow Extended (TFX) is a framework that can create and run end-to-end machine learning pipelines on TensorFlow, a popular library for building and training deep learning models. TFX can preprocess the data, train and evaluate the model, validate and push the model, and serve the model for online or batch predictions. However, using Vertex AI Pipelines and TFX would require writing code, building Docker images, defining pipeline components and steps, and managing the pipeline execution and artifacts. Moreover, TFX is not optimized for image classification, and users would need to use other libraries or tools, such as TensorFlow Data Validation, TensorFlow Transform, and TensorFlow Hub, to handle the image data and the model architecture.

Option D: Converting the image dataset to a tabular format using Dataflow, loading the data into BigQuery, and using BigQuery ML to train the model would not handle the image data properly and could result in a poor model performance. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows. BigQuery is a serverless, scalable, and cost-effec tive data warehouse that can perform fast and interactive queries on large datasets. BigQuery ML is a service that can create and train machine learning models by using SQL queries on BigQuery. However, converting the image data to a tabular format would lose the spatial and semantic information of the images, which are essential for image classification. Moreover, BigQuery ML is not specialized for image classification, and users would need to use other tools or techniques, such as feature hashing, embedding, or one-hot encoding, to handle the categorical features.

Option A is incorrect because using batch prediction mode instead of online mode does not solve the “Out of Memory” error, but rather changes the latency and throughput of the prediction service.  Batch prediction mode is suitable for large-scale, asynchronous, and non-urgent predictions, while online prediction mode is suitable for low-latency, synchronous, and real-time predictions 1 .

Option B is correct because sending the request again with a smaller batch of instances can reduce the memory consumption of the prediction service and avoid the “Out of Memory” error. The batch size is the number of instances that are processed together in one request.  A smaller batch size means less data to load into memory at once 2 .

Option C is incorrect because using base64 to encode your data before using it for prediction does not reduce the memory consumption of the prediction service, but rather increases it.  Base64 encod ing is a way of representing binary data as ASCII characters, which increases the size of the data by about 33% 3 .  Base64 encoding is only required for certain data types, such as images and audio, that cannot be represented as JSON or CSV 4 .

Option D is incorrect because applying for a quota increase for the number of prediction requests does not solve the “Out of Memory” error, but rather increases the number of requests that can be sent to the prediction service per day.  Quotas are limits on the usage of Google Cloud resources, such as CPU, memory, disk, and network 5 . Quotas do not affect the performance of the prediction service, but rather the availability and cost of the service.

 In this scenario, the goal is to create an ML model to predict which newly uploaded videos will be the most popular on a video sharing website. The result that should be used to determine whether the model is successful is the one that best aligns with the business objective and the evaluation metric. Option C is the correct answer because it defines the most popular videos as the ones that have the highest watch time within 30 days of being uploaded, and it sets a high accuracy threshold of 95% for the model prediction.

Option C: The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded. This option is the best result for the scenario because it reflects the business objective and the evaluation metric. The business objective is to prioritize the videos that will attract and retain the most viewers on the website. The watch time is a good indicator of the viewer engagement and satisfaction, as it measures how long the viewers watch the videos. The 30-day window is a reasonable time frame to capture the popularity trend of the videos, as it accounts for the initial interest and the viral potential of the videos. The 95% accuracy threshold is a high standard for the model prediction, as it means that the model can correctly identify 95 out of 100 of the most popular videos based on the watch time metric.

Option A: The model predicts videos as popular if the user who uploads them has over 10,000 likes. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the users who upload them. The number of likes that a user has is not a good indicator of the popularity of their videos, as it does not measure the viewer engagement or satisfaction with the videos. Moreover, this option does not specify a time frame or an accuracy threshold for the model prediction, making it vague and unreliable.

Option B: The model predicts 97.5% of the most popular clickbait videos measured by number of clicks. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most misleading or sensational titles or thumbnails. The number of clicks that a video has is not a good indicator of the popularity of the video, as it does not measure the viewer engagement or satisfaction with the video content. Moreover, this option only focuses on the clickbait videos, which may not represent the majority or the diversity of the videos on the website.

Option D: The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most consistent or inconsistent number of views over time. The Pearson correlation coefficient is a metric that measures the linear relationship between two variables, not the popularity of the videos. A correlation coefficient of 0 means that there is no linear relationship between the log-transformed number of views after 7 days and 30 days, which does not indicate whether the videos are popular or not. Moreover, this option does not specify a threshold or a target value for the correlation coefficient, making it meaningless and irrelevant.

The option A is the most suitable solution for logging data and tracking artifacts from each run of a model development experiment in a Vertex AI Workbench notebook. Vertex AI Workbench is a service that allows you to create and run interactive notebooks on Google Cloud. You can use Vertex AI Workbench to experiment with different preprocessing and modeling approaches for your time series prediction problem. You can also use the Vertex AI TensorBoard instance and the Vertex AI SDK to create an experiment and associate the TensorBoard instance. TensorBoard is a tool that allows you to visualize and monitor the metrics and artifacts of your ML experiments. You can use the Vertex AI SDK to create an experiment object, which is a logical grouping of runs that share a common objective. You can also use the Vertex AI SDK to associate the experiment object with a TensorBoard instance, which is a managed service that hosts a TensorBoard web app. By using the Vertex AI TensorBoard instance and the Vertex AI SDK, you can easily set up and manage your experiments, and access the TensorBoard web app from the Vertex AI console. You can also use the log_time_series_metrics function and the log_metrics function to log data and track artifacts from each run. The log_time_series_metrics function is a function that allows you to log the time series data, such as the multivariate time series and the labels, to the TensorBoard instance. The log_metrics function is a function that allows you to log the scalar metrics, such as the loss values, to the TensorBoard instance. By using these functions, you can record the data and artifacts from each run of your experiment, and compare them in the TensorBoard web app. You can also use the TensorBoard web app to visualize the data and artifacts, such as the time series plots, the scalar charts, the histograms, and the distributions. By using the Vertex AI TensorBoard instance, the Vertex AI SDK, and the log functions, you can log data and track artifacts from each run of your experiment in a Vertex AI Workbench notebook.  References :

Vertex AI Workbench documentation

Vertex AI TensorBoard documentation

Vertex AI SDK documentation

log_time_series_metrics function documentation

log_metrics function documentation

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According to the official exam gui de 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”.  Document AI 2  is a document understanding platform that takes unstructured data from documents and transforms it into structured data, making it easier to understand, analyze, and consume.  Documen t AI Workbench 3  allows you to create custom extractors to parse the text in specific sections of your documents.  Natural Language API 4  is a service that provides natural language understanding technologies, such as sentiment analysis, entity analysis, and other text annotations.  The analyzeSentiment feature 5  inspects the given text and identifies the prevailing emotional opinion within the text, especially to determine a writer’s attitude as positive, negative, or neutral. Therefore, option C is the best way to accomplish the task of predicting an overall satisfaction score from the customer comments on each form. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Document AI

Document AI Workbench

Natural Language API

Sentiment analysis

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Ac tual Free Exam Questions

In the context of MLOps on Google Cloud and Vertex AI, the goal is to balance model performance with operational efficiency. Here is why Option D is the correct strategy for minimizing cost and effort while maintaining reliability:

Data Drift and Model Decay: In production environments, the distribution of live data often changes over time (a phenomenon known as Training-Serving Skew or Data Drift ). If the customer attributes in the real world no longer match the data the model was trained on, the model’s predictive power will degrade.

Vertex AI Model Monitoring: Vertex AI provides built-in tools to monitor for Feature Attribution Drift and Training-Serving Skew . By setting up alerts for these shifts, you implement " Performance-based " or " Condition-based " retraining. This is more cost-effective than retraining every week (Option C), which might use expensive compute resources to retrain a model that is still performing perfectly.

Why other options are incorrect:

Option A: Latency is an infrastructure/engineering metric, not a predictive quality metric. Retraining the model will not fix latency issues caused by high traffic; that would require scaling your prediction nodes.

Option B: While accuracy is important, waiting for a 10% drop on a new dataset often means the model has already been underperforming in production for some time. Furthermore, calculating accuracy requires " ground truth " (actual labels), which may not be available immediately for churn.

Option C: Retraining weekly regardless of performance leads to unnecessary compute costs and engineering overhead if the data hasn ' t changed significantly.

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]

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