Google Professional-Machine-Learning-Engineer Dumps Questions Answers

Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.

Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri

environment variable to read the images by using the tf. data. Dataset. Iist_flies function.

Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.

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.

Set up a TensorFlow Extended (TFX) pipeline on Vertex Al Pipelines to orchestrate the MLOps pipeline. Write a custom component for the PySpark-based workloads on Dataproc.

Set up a Vertex Al Pipelines to orchestrate the MLOps pipeline. Use the predefined Dataproc component for the PySpark-based workloads.

Set up Cloud Composer to orchestrate the MLOps pipeline. Use Dataproc workflow templates for the PySpark-based workloads in Cloud Composer.

Set up Kubeflow Pipelines on Google Kubernetes Engine to orchestrate the MLOps pipeline. Write a custom component for the PySpark-based workloads on Dataproc.