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Google Professional Machine Learning Engineer Practice Test - Questions Answers, Page 19

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Question 181

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You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for

Question 182

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You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training

Question 183

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You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workf

Question 184

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You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for

Question 185

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You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that wil

Question 186

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You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the mode

Question 187

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Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipelin

Question 188

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You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were

Question 189

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Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will inc

Question 190

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You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project.

Related questions

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

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?

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?

You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?

You are an AI architect at a popular photo-sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?

You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery, processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex Al Pipelines. Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to avoid additional costs. What should you do?

You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? Choose 2 answers

Question 181

You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.

Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.

Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.

Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

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Question 182

You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

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Question 183

You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do?

Use the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex Al services Deploy the workflow on Cloud Composer.

Use the MLFlow SDK and deploy it on a Google Kubernetes Engine Cluster Create multiple components that use Dataflow and Vertex Al services.

Use the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

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

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Question 184

You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for online inference. You want to use the simplest approach. What should you do?

Use the Vertex Al REST API within a custom component based on a vertex-ai/prediction/xgboost-cpu image.

Use the Vertex Al ModelEvaluationOp component to evaluate the model.

Use the Vertex Al SDK for Python within a custom component based on a python: 3.10 Image.

Chain the Vertex Al ModelUploadOp and ModelDeployop components together.

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Question 185

You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

Use Prophet on Vertex Al Training to build a custom model.

Use Vertex Al Forecast to build a NN-based model.

Use BigQuery ML to build a statistical AR1MA_PLUS model.

Use TensorFlow on Vertex Al Training to build a custom model.

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Question 186

You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

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Question 187

Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?

Configure a Cloud Build trigger with the event set as 'Pull Request'

Configure a Cloud Build trigger with the event set as 'Push to a branch'

Configure a Cloud Function that builds the repository each time there is a code change.

Configure a Cloud Function that builds the repository each time a new branch is created.

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Question 188

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

Use a machine type with more memory

Decrease the number of workers per machine

Increase the CPU utilization target in the autoscaling configurations

Decrease the CPU utilization target in the autoscaling configurations

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Question 189

Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.

1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.

2 Predictions will be stored in BigQuery

3. The model will be stored in a Cloud Storage bucket and will be updated frequently

You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.

Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.

Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.

Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

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Question 190

You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

1. Create a new service account and grant it the Notebook Viewer role. 2 Grant the Service Account User role to each team member on the service account. 3 Grant the Vertex Al User role to each team member. 4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

1. Grant the Vertex Al User role to the default Compute Engine service account. 2. Grant the Service Account User role to each team member on the default Compute Engine service account. 3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.

1 Create a new service account and grant it the Vertex Al User role. 2 Grant the Service Account User role to each team member on the service account. 3. Grant the Notebook Viewer role to each team member. 4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

1 Grant the Vertex Al User role to the primary team member. 2. Grant the Notebook Viewer role to the other team members. 3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.

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