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Google Professional Machine Learning Engineer Practice Test - Questions Answers, Page 29

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Question 281

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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

Question 282

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You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predict

Question 283

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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. Y

Question 284

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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 conta

Question 285

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Your organization's marketing team is building a customer recommendation chatbot that uses a generative AI large language model (LLM) to provide personalized product suggestions in real time. The ch

Related questions

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?

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?

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 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 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

You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

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?

Question 281

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

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

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

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

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You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model. What should you do?

Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an ARIMA model.

Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an ARIMA model.

Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an AutoML regression model.

Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an AutoML regression model.

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Question 283

Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.

Train an image clustering model using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.

Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy it to a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.

Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.

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Question 284

Use Vertex AI's prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.

Use Vertex AI's prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.

Deploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.

Deploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.

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Question 285

Your organization's marketing team is building a customer recommendation chatbot that uses a generative AI large language model (LLM) to provide personalized product suggestions in real time. The chatbot needs to access data from millions of customers, including purchase history, browsing behavior, and preferences. The data is stored in a Cloud SQL for PostgreSQL database. You need the chatbot response time to be less than 100ms. How should you design the system?

Use BigQuery ML to fine-tune the LLM with the data in the Cloud SQL for PostgreSQL database, and access the model from BigQuery.

Replicate the Cloud SQL for PostgreSQL database to AlloyDB. Configure the chatbot server to query AlloyDB.

Transform relevant customer data into vector embeddings and store them in Vertex AI Search for retrieval by the LLM.

Create a caching layer between the chatbot and the Cloud SQL for PostgreSQL database to store frequently accessed customer data. Configure the chatbot server to query the cache.

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