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Machine Learning Engineer Google Professional Machine Learning Engineer

Google Professional Machine Learning Engineer

Last Update Jul 30, 2025
Total Questions : 285

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Questions 2

You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

Options:

A.  

Compare the loss performance for each model on a held-out dataset.

B.  

Compare the loss performance for each model on the validation data

C.  

Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool

D.  

Compare the mean average precision across the models using the Continuous Evaluation feature

Discussion 0
Questions 3

You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

Options:

A.  

Create an object detection model that can localize the rust spots.

B.  

Develop an image segmentation ML model to locate the boundaries of the rust spots.

C.  

Develop a template matching algorithm using traditional computer vision libraries.

D.  

Develop an image classification ML model to predict the presence of the disease.

Discussion 0
Questions 4

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?

Options:

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 prediction

2. 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 tool

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 BigQuery.

Discussion 0
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Questions 5

You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

Options:

A.  

Normalize the data using Google Kubernetes Engine

B.  

Translate the normalization algorithm into SQL for use with BigQuery

C.  

Use the normalizer_fn argument in TensorFlow's Feature Column API

D.  

Normalize the data with Apache Spark using the Dataproc connector for BigQuery

Discussion 0
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