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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 186–190 (page: 39)

Updated On: 25-Oct-2026
Page 39 of 82
PDF with 402 Questions

You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production.
What is the most streamlined and reliable way to perform this validation?

  1. Use then TFX ModelValidator tools to specify performance metrics for production readiness.
  2. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
  3. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data.
  4. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

Answer(s): A



You work for an organization that operates a streaming music service. You have a custom production model that is serving a “next song” recommendation based on a user's recent listening history. Your model is deployed on a Vertex AI endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity.
What should you do?

  1. Create a new Vertex AI endpoint for the new model and deploy the new model to that new endpoint. Build a service to randomly send 5% of production traffic to the new endpoint. Monitor end-user metrics such as listening time. If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new endpoint.
  2. Capture incoming prediction requests in BigQuery. Create an experiment in Vertex AI Experiments. Run batch predictions for both models using the captured data. Use the user’s selected song to compare the models performance side by side. If the new model’s performance metrics are better than the previous model, deploy the new model to production.
  3. Deploy the new model to the existing Vertex AI endpoint. Use traffic splitting to send 5% of production traffic to the new model. Monitor end-user metrics, such as listening time. If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.
  4. Configure a model monitoring job for the existing Vertex AI endpoint. Configure the monitoring job to detect prediction drift and set a threshold for alerts. Update the model on the endpoint from the previous model to the new model. If you receive an alert of prediction drift, revert to the previous model.

Answer(s): C



You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn’t changed; however the accuracy of the model has steadily deteriorated.
What issue is most likely causing the steady decline in model accuracy?

  1. Poor data quality
  2. Lack of model retraining
  3. Too few layers in the model for capturing information
  4. Incorrect data split ratio during model training, evaluation, validation, and test

Answer(s): B



You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

  1. Configure a v3-8 TPU VM. SSH into the VM to train and debug the model.
  2. Configure a v3-8 TPU node. Use Cloud Shell to SSH into the Host VM to train and debug the model.
  3. Configure a n1 -standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use ParameterServerStraregv to train the model.
  4. Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model.

Answer(s): D



Your team is developing a customer support chatbot for a healthcare company that processes sensitive patient information. You need to ensure that all personally identifiable information (PII) captured during customer conversations is protected prior to storing or analyzing the data.
What should you do?

  1. Use the Cloud Natural Language API to identify and redact PII in chatbot conversations.
  2. Use the Cloud Natural Language API to classify and categorize all data, including PII, in chatbot conversations.
  3. Use the DLP API to encrypt PII in chatbot conversations before storing the data.
  4. Use the DLP API to scan and de-identify PII in chatbot conversations before storing the data.

Answer(s): D

Explanation:

DLP API (Data Loss Prevention API) is specifically designed to identify, classify, and protect sensitive information like PII. It can scan chatbot conversations for PII and perform de-identification operations such as masking, tokenization, or redaction to ensure data privacy.
De-identifying PII before storage ensures compliance with data protection regulations such as HIPAA (for healthcare data) and enhances data security by removing or obfuscating sensitive information.



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