Free Practice Google Professional-Machine-Learning-Engineer Exam Questions 2025

Stay ahead with 100% Free Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Dumps Practice Questions

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Total 289 Questions | Updated On: Mar 27, 2025

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Question 1

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

Question 2

You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

A : Use Principal Component Analysis to eliminate the least informative features.

B : Use L1 regularization to reduce the coefficients of uninformative features to 0.

C : After building your model, use Shapley values to determine which features are the most informative.

D : Use an iterative dropout technique to identify which features do not degrade the model when removed.

Answer: B

Question 3

You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

A : Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization

B : Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.

C : Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.

D : Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization

Answer: A

Question 4

You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

A : Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient" and cookware" and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.

B : Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model’s performance on a holdout dataset.

C : Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.

D : Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.

Answer: A

Question 5

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

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

C : Enable early stopping in your Vertex AI Training job.

D : Use the tf.distribute.Strategy API and run a distributed training job.

Answer: C

Page: 1 / 58

Total 289 Questions | Updated On: Mar 27, 2025

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