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: May 23, 2026

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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?

A : clude the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

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

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

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

Answer: C

Question 2

You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

A : crease the instance memory to 512 GB and increase the batch size.

B : place the NVIDIA P100 GPU with a v3-32 TPU in the training job.

C : able early stopping in your Vertex AI Training job

D : able early stopping in your Vertex AI Training job

Answer: C

Question 3

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)

What is the correct way to write the predict request?

A : ta = json.dumps({ג€signature_nameג€: ג€seving_defaultג€, ג€instancesג€ [['ab', 'bc', 'cd']]})

B : ta = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c', 'd', 'e', 'f']]})

C : ta = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c'], ['d', 'e', 'f']]})

D : ta = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b'], ['c', 'd'], ['e', 'f']]})

Answer: C

Question 4

You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

A :

B :

C :

D :

Answer: A

Question 5

You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

A : Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

B : Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution skew

C : Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

D : Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.

Answer: A

Page: 1 / 58

Total 289 Questions | Updated On: May 23, 2026

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