Free Google Professional-Machine-Learning-Engineer Exam Questions

Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers

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Total 289 Questions | Updated On: Feb 21, 2026

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Question 1

You work on a team in a data center that is responsible for server maintenance. Your management team wants you to build a predictive maintenance solution that uses monitoring data to detect potential server failures. Incident data has not been labeled yet. What should you do first?

A : ain a time-series model to predict the machines’ performance values. Configure an alert if a machine’s actual performance values significantly differ from the predicted performance values.

B : velop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Use this heuristic to monitor server performance in real time.

C : velop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Train a model to predict anomalies based on this labeled dataset.

D : re a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.

Answer: D

Question 2

You work for a bank You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex Al services to include in the workflow You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex Al services should you use?

A : Vertex ML Metadata Vertex Al Feature Store, and Vertex Al Vizier

B : Vertex Al Pipelines. Vertex Al Experiments, and Vertex Al Vizier

C : Vertex ML Metadata Vertex Al Experiments, and Vertex Al TensorBoard

D : Vertex Al Pipelines. Vertex Al Feature Store, and Vertex Al TensorBoard

Answer: D

Question 3

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?

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

B : Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs

C : Deploy a matrix factorization model training job by using BigQuery ML.

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

Answer: A

Question 4

You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

A : eate a hot-encoding of words, and feed the encodings into your model.

B : entify word embeddings from a pre-trained model, and use the embeddings in your model.

C : rt the words by frequency of occurrence, and use the frequencies as the encodings in your model.

D : ign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model

Answer: B

Question 5

You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A : Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job

B : Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code

C : Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository

D : Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Answer: C

Page: 1 / 58

Total 289 Questions | Updated On: Feb 21, 2026

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