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: Mar 24, 2026

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Question 1

Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

A : Add synthetic training data where those phrases are used in non-toxic ways.

B : move the model and replace it with human moderation.

C : place your model with a different text classifier.

D : ise the threshold for comments to be considered toxic or harmful.

Answer: A

Question 2

You work for a food product company. Your company’s historical sales data is stored in BigQuery.You need to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs mm-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?

A : Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.

B : Write SQL queries to transform the data in-place in BigQuery.

C : Add the transformations as a preprocessing layer in the TensorFlow models.

D : Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.

Answer: B

Question 3

You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)

A : move training examples of high-performing subgroups, and retrain the model.

B : an additional objective to penalize the model more for errors made on the minority class, and retrain the model

C : move the features that have the highest correlations with the majority class.

D : ample or reweight your existing training data, and retrain the model

E : deploy the model, and provide a label explaining the model's behavior to users.

Answer: B,D

Question 4

During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A : crease the size of the training batch.

B : crease the learning rate hyperparameter.

C : crease the learning rate hyperparameter.

Answer: C

Question 5

You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor’s data from the past 12 hours. How should you design the architecture?

A : 1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction2. Your application queries a Vertex AI endpoint where you deployed your model.3. Responses are received by the caller application as soon as the model produces the prediction.

B : 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

C : 1. Export your data to Cloud Storage using Dataflow.2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL

D : 1. Export the data to Cloud Storage using the BigQuery command-line tool2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.

Answer: C

Page: 1 / 58

Total 289 Questions | Updated On: Mar 24, 2026

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