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Google Professional Machine Learning Engineer

Last Update Dec 26, 2024
Total Questions : 285 With Methodical Explanation

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Google Professional Machine Learning Engineer Questions and Answers

Questions 1

You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

Options:

A.

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.

B.

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.

C.

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

D.

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

Questions 2

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?

Options:

A.

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

B.

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

C.

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

D.

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.

Questions 3

You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evaluation, and model deployment. Your organization primarily uses PySpark-based workloads for data preprocessing. You want to minimize infrastructure management effort. How should you set up the pipeline?

Options:

A.

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.

B.

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

C.

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

D.

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

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