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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 126–130 (page: 27)

Updated On: 25-Oct-2026
Page 27 of 82
PDF with 402 Questions

You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images. You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs.
What should you do?

  1. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs. Prepare and submit a TFJob operator to this node pool.
  2. Create a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model.
  3. Package your code with Setuptools, and use a pre-built container. Train your model with Vertex AI using a custom tier that contains the required GPUs.
  4. Configure a Compute Engine VM with all the dependencies that launches the training. Train your model with Vertex AI using a custom tier that contains the required GPUs.

Answer(s): C



You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company’s product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use AI Platform’s continuous evaluation service to ensure that the models have high accuracy on your test dataset.
What should you do?

  1. Keep the original test dataset unchanged even if newer products are incorporated into retraining.
  2. Extend your test dataset with images of the newer products when they are introduced to retraining.
  3. Replace your test dataset with images of the newer products when they are introduced to retraining.
  4. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

Answer(s): B



You are training and deploying updated versions of a regression model with tabular data by using Vertex AI Pipelines, Vertex AI Training, Vertex AI Experiments, and Vertex AI Endpoints. The model is deployed in a Vertex AI endpoint, and your users call the model by using the Vertex AI endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model.
What should you do?

  1. Use Vertex AI Model Monitoring. Enable prediction drift monitoring on the endpoint, and specify a notification email.
  2. In Cloud Logging, create a logs-based alert using the logs in the Vertex AI endpoint. Configure Cloud Logging to send an email when the alert is triggered.
  3. In Cloud Monitoring, create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.
  4. Export the container logs of the endpoint to BigQuery. Create a Cloud Run function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Run function.

Answer(s): A



You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than 20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?

  1. Add a regularization term such as the Min-Diff algorithm to the loss function.
  2. Train a classifier using the chat messages in their original language.
  3. Replace the in-house word2vec with GPT-3 or T5.
  4. Remove moderation for languages for which the false positive rate is too high.

Answer(s): B



You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization.
What should you do?

  1. Embed the augmentation functions dynamically in the tf.Data pipeline.
  2. Embed the augmentation functions dynamically as part of Keras generators.
  3. Use Dataflow to create all possible augmentations, and store them as TFRecords.
  4. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.

Answer(s): A



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