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: Apr 16, 2026

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Question 1

You work for a retail company. You have a managed tabular dataset in Vertex Al that contains sales data from three different stores. The dataset includes several features such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon You need to split the data between the training, validation, and test sets What approach should you use to split the data?

A : Use Vertex Al manual split, using the store name feature to assign one store for each set.

B : Use Vertex Al default data split.

C : Use Vertex Al chronological split and specify the sales timestamp feature as the time vanable.

D : Use Vertex Al random split assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set.

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 work at a leading healthcare firm developing state-of-the-art algorithms for various use cases You have unstructured textual data with custom labels You need to extract and classify various medical phrases with these labels What should you do?

A : Use the Healthcare Natural Language API to extract medical entities.

B : Use a BERT-based model to fine-tune a medical entity extraction model.

C : Use AutoML Entity Extraction to train a medical entity extraction model.

D : Use TensorFlow to build a custom medical entity extraction model.

Answer: B

Question 4

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 5

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

Page: 1 / 58

Total 289 Questions | Updated On: Apr 16, 2026

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