Practice Professional-Machine-Learning-Engineer Exam Online

Question 1

You are developing a training pipeline for a new XGBoost classification model based on tabular data The data

is stored in a BigQuery table You need to complete the following steps

1. Randomly split the data into training and evaluation datasets in a 65/35 ratio

2. Conduct feature engineering

3 Obtain metrics for the evaluation dataset.

4 Compare models trained in different pipeline executions

How should you execute these steps'?

A : 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and

add another component for feature engineering

2. Enable auto logging of metrics in the training component.

3 Compare pipeline runs in Vertex Al Experiments

B : 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and

add another component for feature engineering

2 Enable autologging of metrics in the training component

3 Compare models using the artifacts lineage in Vertex ML Metadata

C : 1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model

type and use BigQuery to handle the data splits.

2 Use a SQL view to apply feature engineering and train the model using the data in that view

3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc

statement.

D : 1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model

type, and use BigQuery to handle the data splits.

2 Use ml transform to specify the feature engineering transformations, and train the model using the

data in the table

' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info

statement.

Answer: B

Question 2

You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A : Use Vertex Explainable Al for model explainability Configure feature-based explanations.

B : Use Vertex Explainable Al for model explainability Configure example-based explanations.

C : Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.

D :

Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.

Answer: C

Question 3

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A : Use the batch prediction functionality of AI Platform.

B : Create a serving pipeline in Compute Engine for prediction.

C : Use Cloud Functions for prediction each time a new data point is ingested

D : Deploy the model on AI Platform and create a version of it for online inference.

Answer: A

Question 4

You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human. Which metric(s) should you use to monitor the model’s performance?

A : Number of messages flagged by the model per minute

B : Number of messages flagged by the model per minute confirmed as being inappropriate by humans.

C : Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review

D : Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute

Answer: D

Question 5

You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

A : Store features in Bigtable as key/value data.

B : Store features in Vertex Al Feature Store.

C :

Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.

D :

Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Answer: B

Name:	Professional Machine Learning Engineer
Exam Code:	Professional-Machine-Learning-Engineer
Certification:	Google Cloud Certified
Vendor:	Google
Total Questions:	289
Last Updated:	Apr 01, 2026

Google Professional-Machine-Learning-Engineer Answers