Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

Exam Code:

Professional-Machine-Learning-Engineer

Exam Name:

Google Professional Machine Learning Engineer

Certification:

Machine Learning Engineer

Vendor:

Google

Questions:

285

Last Updated:

Feb 11, 2026

Exam Status:

Stable

Professional-Machine-Learning-Engineer: Machine Learning Engineer Exam 2025 Study Guide Pdf and Test Engine

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Google Professional Machine Learning Engineer Questions and Answers

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Question 1

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

Options:

Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

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Answer:

Explanation:

The best option to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead is to use Dataflow as the runner for the evaluation step. Dataflow is a fully managed service for executing Apache Beam pipelines that can scale up and down according to the workload. Dataflow can handle large-scale, distributed data processing tasks such as model evaluation, and it can also integrate with Vertex AI Pipelines and TensorFlow Extended (TFX). By using the flag -runner=DataflowRunner in beam_pipeline_args, you can instruct the Evaluator component to run the evaluation step on Dataflow, instead of using the default DirectRunner, which runs locally and may cause out-of-memory errors. Option A is incorrect because adding tfma.MetricsSpec() to limit the number of metrics in the evaluation step may downgrade the evaluation quality, as some important metrics may be omitted. Moreover, reducing the number of metrics may not solve the out-of-memory error, as the evaluation step may still consume a lot of memory depending on the size and complexity of the data and the model. Option B is incorrect because migrating the pipeline to Kubeflow hosted on Google Kubernetes Engine (GKE) may increase the infrastructure overhead, as you need to provision, manage, and monitor the GKE cluster yourself. Moreover, you need to specify the appropriate node parameters for the evaluation step, which may require trial and error to find the optimal configuration. Option D is incorrect because moving the evaluation step out of the pipeline and running it on custom Compute Engine VMs may also increase the infrastructure overhead, as you need to create, configure, and delete the VMs yourself. Moreover, you need to ensure that the VMs have sufficient memory for the evaluation step, which may require trial and error to find the optimal machine type. References:

Dataflow documentation

Using DataflowRunner

Evaluator component documentation

Configuring the Evaluator component

Question 2

You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

Options:

Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.

Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.

Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model

output.

Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.

Question 3

You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline'?