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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 206–210 (page: 43)

Updated On: 25-Oct-2026
Page 43 of 82
PDF with 402 Questions

You need to develop a custom TensorFlow model that will be used for online predictions. The training data is stored in BigQuery You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

  1. Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
  2. Create a pipeline in Vertex AI Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
  3. Create a preprocessing function that reads and transforms the data from BigQuery. Create a Vertex AI custom prediction routine that calls the preprocessing function at serving time.
  4. Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

Answer(s): D



You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model’s performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost.
What should you do?

  1. Train an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service.
  2. Identify temporal patterns in your model’s performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year.
  3. Compare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.
  4. Run training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service.

Answer(s): D



You developed a demand forecasting model using BigQuery ML. New sales data is ingested into a BigQuery table daily. You want to implement an automated retraining strategy where the model is evaluated against the previous week’s data every Sunday, and the model is retrained if the mean absolute error (MAE) exceeds 500 units. You want to use native BigQuery capabilities to automate this workflow and minimize external dependencies.
What should you do?

  1. Create an Agent Platform Pipelines workflow containing a BigQueryJobOp component that runs the ML.EVALUATE statement. Add a conditional step to check the MAE and conditionally execute a training step. Schedule the pipeline using Agent Platform Pipelines.
  2. Write a CREATE OR REPLACE MODEL statement with the OPTIONS clause set to warm_start = TRUE. Schedule the query to run every Sunday using a BigQuery scheduled query.
  3. Create a Cloud Run function triggered by a Cloud Scheduler job. Configure the function to use the BigQuery client library to run the evaluation query. If the MAE threshold is reached, issue a second API call to retrain the model.
  4. Use BigQuery procedural language to write a query that runs the ML.EVALUATE statement, stores the MAE in a variable, and uses an IF statement to conditionally execute the CREATE OR REPLACE MODEL statement. Schedule the script using a BigQuery scheduled query.

Answer(s): D

Explanation:

BigQuery procedural SQL can evaluate the model, assign the resulting MAE to a variable, and use conditional logic to retrain only when the threshold is exceeded. Scheduling the script as a weekly BigQuery scheduled query keeps the entire workflow within BigQuery and minimizes external services and dependencies.



You have trained a PyTorch model to classify customer reviews. The model requires the input text to be tokenized and vectorized before inference. The client application sends the raw text of the review in a REST API request. You need to deploy the model to Gemini Enterprise Agent Platform for online prediction. You want to implement the preprocessing logic such that it minimizes prediction latency and ensures the preprocessing code is versioned strictly along with the model artifacts.
What should you do?

  1. Use the Agent Platform custom inference routines to build a container that includes a custom predictor class with the tokenization logic. Deploy this container to an Agent Platform endpoint.
  2. Upload the tokenizer vocabulary to Agent Platform Feature Store. Configure the Agent Platform endpoint to retrieve the vocabulary and process the raw text during the prediction call.
  3. Deploy the model to an Agent Platform endpoint using a prebuilt PyTorch container. Update the client application to tokenize the text locally before sending the request to the endpoint.
  4. Deploy the model to an Agent Platform endpoint using a prebuilt PyTorch container. Create a Cloud Run function to tokenize the raw text and pass the vectors to the endpoint.

Answer(s): A

Explanation:

Agent Platform custom inference routines allow you to implement custom preprocessing and prediction logic within the same serving container. By defining a custom predictor class that performs text tokenization, vectorization, and PyTorch model inference, the endpoint can accept raw customer reviews directly through REST API requests. Packaging the preprocessing logic, tokenizer dependencies, and model artifacts together ensures consistent versioning and reproducible predictions. Executing preprocessing within the prediction container also minimizes latency by eliminating additional network calls to external preprocessing services, providing an efficient and maintainable solution for online inference.



You work at a bank. You have a custom tabular ML model that was provided by the bank’s vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex AI Model serving container, which accepts a string as input for each prediction instance. In each string, the feature values are separated by commas. You want to deploy this model to production for online predictions and monitor the feature distribution over time with minimal effort.
What should you do?

  1. 1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
    2. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema
  2. 1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
    2. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and provide an instance schema
  3. 1. Refactor the serving container to accept key-value pairs as input format
    2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
    3. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective.
  4. 1. Refactor the serving container to accept key-value pairs as input format
    2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
    3. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective

Answer(s): A



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