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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 81–85 (page: 18)

Updated On: 25-Oct-2026
Page 18 of 82
PDF with 402 Questions

You are using generative AI to build a conversational agent to support bank customers’ inquiries about their product base and transaction history. You must ensure that the agent does not include personally identifiable information (PII) in its responses. You want to implement this requirement with minimal effort.
What should you do?

  1. Configure the safety_settings thresholds within the Agent Platform model parameters to Block_Most.
  2. Develop a post-processing wrapper using Python’s re module with patterns r'\b(?:\d{4}[- ]?){3}\d{4}\b' to identify and redact credit card numbers.
  3. Implement Model Armor with Sensitive Data Protection to detect and redact PII from the agent’s responses.
  4. Use sampled Shapley (SHAP) values to understand the model’s predictions and ensure that it is not biased toward revealing PII.

Answer(s): C

Explanation:

Model Armor integrates with Sensitive Data Protection to inspect generated responses for personally identifiable information and automatically redact or block sensitive content. This provides managed, policy-based protection across multiple PII types with minimal custom development.



You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100,000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training, and are stored in a Cloud Storage bucket. You need to be able to tune the model during each training run. How should you train the model?

  1. Train a model for object detection by using Vertex AI AutoML.
  2. Train a model for image classification by using Vertex AI AutoML.
  3. Develop the model training code for object detection, and train a model by using Vertex AI custom training.
  4. Develop the model training code for image classification, and train a model by using Vertex AI custom training.

Answer(s): D



You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time.
What should you do?

  1. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
  2. Create a custom training loop.
  3. Use a TPU with tf.distribute.TPUStrategy.
  4. Increase the batch size.

Answer(s): D



You are developing an ML model intended to classify whether X-ray images indicate bone fracture risk. You have trained a ResNet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the training time and memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the model’s accuracy.
What should you do?

  1. Reduce the number of layers in the model architecture.
  2. Reduce the global batch size from 1024 to 256.
  3. Reduce the dimensions of the images used in the model.
  4. Configure your model to use bfloat16 instead of float32.

Answer(s): D



Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

  1. Distribute texts randomly across the train-test-eval subsets:
    Train set: [TextA1, TextB2, ...]
    Test set: [TextA2, TextC1, TextD2, ...]
    Eval set: [TextB1, TextC2, TextD1, ...]
  2. Distribute authors randomly across the train-test-eval subsets: (*)
    Train set: [TextA1, TextA2, TextD1, TextD2, ...]
    Test set: [TextB1, TextB2, ...]
    Eval set: [TexC1,TextC2 ...]
  3. Distribute sentences randomly across the train-test-eval subsets:
    Train set: [SentenceA11, SentenceA21, SentenceB11, SentenceB21, SentenceC11, SentenceD21 ...] Test set: [SentenceA12, SentenceA22, SentenceB12, SentenceC22, SentenceC12, SentenceD22 ...] Eval set: [SentenceA13, SentenceA23, SentenceB13, SentenceC23, SentenceC13, SentenceD31 ...]
  4. Distribute paragraphs of texts (i.e., chunks of consecutive sentences) across the train-test-eval subsets: Train set: [SentenceA11, SentenceA12, SentenceD11, SentenceD12 ...]
    Test set: [SentenceA13, SentenceB13, SentenceB21, SentenceD23, SentenceC12, SentenceD13 ...] Eval set: [SentenceA11, SentenceA22, SentenceB13, SentenceD22, SentenceC23, SentenceD11 ...]

Answer(s): B



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