Instant Access Google.Professional-Machine-Learning-Engineer.v2026-07-28.q219 Actual Practice Test Engine for Free (Page 23)

Question 106

You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model's underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

A.Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.
B.Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.
C.Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.
D.Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.

Question 107

You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

A.Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.
B.Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.
C.Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.
D.Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.

Question 108

You recently deployed a pipeline in Vertex AI Pipelines that trains and pushes a model to a Vertex AI endpoint to serve real-time traffic. You need to continue experimenting and iterating on your pipeline to improve model performance. You plan to use Cloud Build for CI/CD You want to quickly and easily deploy new pipelines into production, and you want to minimize the chance that the new pipeline implementations will break in production. What should you do?

A.Set up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre- production environment. Run unit tests in the pre-production environment. If the tests are successful deploy the pipeline to production.
B.Set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, deploy the pipeline to production.
C.Set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, rebuild the source code and deploy the artifacts to production.
D.Set up a CI/CD pipeline that builds and tests your source code. If the tests are successful, use the Google. Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines.

Question 109

You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?

A.Compare the training and evaluation losses of the current run If the losses are similar, deploy the model to a Vertex AI endpoint Configure a cron job to redeploy the pipeline every night.
B.Compare the training and evaluation losses of the current run If the losses are similar deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring When the model monitoring threshold is tnggered redeploy the pipeline.
C.Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint Configure a cron job to redeploy the pipeline every night.
D.Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.

Question 110

You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist's local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost. What should you do?

A.Move the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and schedule the execution of the steps in the Notebooks instance using Cloud Scheduler.
B.Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining.
C.Rewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of the job on ephemeral Dataproc clusters using Cloud Scheduler.
D.Extract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an Apache Airflow BashOperator, and run the resulting directed acyclic graph (DAG) in Cloud Composer.

Correct Answer: B

The best option for productionizing a Keras model is to use TensorFlow Extended (TFX), a framework for building end-to-end machine learning pipelines that can handle large-scale data and complex workflows. TFX provides standard components for data ingestion, transformation, validation, analysis, training, tuning, serving, and monitoring. TFX pipelines can be orchestrated with Vertex AI Pipelines, a managed service that runs on Google Cloud Platform and leverages Kubernetes and Argo. Vertex AI Pipelines allows you to automate the execution of your TFX pipeline steps, schedule retraining jobs, and scale up or down the resources as needed.
By using TFX and Vertex AI Pipelines, you can take advantage of the following benefits:
* You can reuse the existing code in your Jupyter notebook, as TFX supports Keras as a first-class citizen.
You can also use the Keras Tuner to optimize your model hyperparameters.
* You can ensure data quality and consistency by using the TFX Data Validation component, which can detect anomalies, drift, and skew in your data. You can also use the TFX SchemaGen component to generate a schema for your data and enforce it throughout the pipeline.
* You can analyze your model performance and fairness by using the TFX Model Analysis component, which can produce various metrics and visualizations. You can also use the TFX Model Validation component to compare your new model with a baseline model and set thresholds for deploying the model to production.
* You can deploy your model to various serving platforms by using the TFX Pusher component, which can push your model to Vertex AI, Cloud AI Platform, TensorFlow Serving, or TensorFlow Lite. You can also use the TFX Model Registry to manage the versions and metadata of your models.
* You can monitor your model performance and health by using the TFX Model Monitor component, which can detect data drift, concept drift, and prediction skew in your model. You can also use the TFX Evaluator component to compute metrics and validate your model against a baseline or a slice of data.
* You can reduce the cost and complexity of managing your own infrastructure by using Vertex AI Pipelines, which provides a serverless environment for running your TFX pipeline. You can also use the Vertex AI Experiments and Vertex AI TensorBoard to track and visualize your pipeline runs.
References:
* [TensorFlow Extended (TFX)]
* [Vertex AI Pipelines]
* [TFX User Guide]

Question 106

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Question 109

Question 110

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