Google Professional Machine Learning Engineer Exam Questions - Navigate Your Path to Success

The Google Professional Machine Learning Engineer (Professional Machine Learning Engineer) exam is a good choice for Machine Learning Engineers Google Cloud Engineers and if the candidate manages to pass Google Professional Machine Learning Engineer exam, he/she will earn Google Cloud Certified, Google Cloud Certified - Cloud Engineer Certifications. Below are some essential facts for Google Professional Machine Learning Engineer exam candidates:

TrendyCerts offers 283 Questions that are based on actual Google Professional Machine Learning Engineer syllabus.
Our Google Professional Machine Learning Engineer Exam Practice Questions were last updated on: Feb 28, 2025

Sample Questions for Google Professional Machine Learning Engineer Exam Preparation

Question 1

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?

AUse Vertex Al Data Labeling Service to label the images and train an AutoML image classification model.
Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class.

BUse Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.

CConvert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.

DImport the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.

Correct : A

Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as ''pass'' or ''fail'', based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy.Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.

Vertex AI | Google Cloud

Vertex AI Data Labeling Service | Google Cloud

Vertex AI AutoML Image Classification | Google Cloud

Pub/Sub | Google Cloud

Options Selected by Other Users:

Question 2

You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100 000 categorical features. You notice that as the data increases the model training time increases. You plan to move the models to Google Cloud You want to use the most scalable approach that also minimizes training time. What should you do?

ADeploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.

BDeploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs

CDeploy a matrix factorization model training job by using BigQuery ML.

DDeploy the training jobs by using Compute Engine instances with A100 GPUs and use the
t f. nn. embedding_lookup API.

Correct : A

TPU VMs with TPUv3 Pod slices are the most scalable and performant option for training large-scale recommender models on Google Cloud. TPUv3 Pods can provide up to 2048 cores and 32 TB of memory, and can process billions of examples and features in minutes. The TPUEmbedding API is designed to efficiently handle large-scale categorical features and embeddings, and can reduce the memory footprint and communication overhead of the model. The other options are either less scalable (B and C) or less efficient (D) for this use case.