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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 76–80 (page: 17)

Updated On: 25-Oct-2026
Page 17 of 82
PDF with 402 Questions

You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an “Out of Memory” error.
What should you do?

  1. Use batch prediction mode instead of online mode.
  2. Send the request again with a smaller batch of instances.
  3. Use base64 to encode your data before using it for prediction.
  4. Apply for a quota increase for the number of prediction requests.

Answer(s): B



One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this.
What should you do?

  1. Use TensorFlow Data Validation to detect and flag schema anomalies.
  2. Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don’t match the schema with 0.
  3. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
  4. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

Answer(s): A



You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

  1. Configure your pipeline with Dataflow, which saves the files in Cloud Storage. After the file is saved, start the training job on a GKE cluster.
  2. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files. As soon as a file arrives, initiate the training job.
  3. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster.
  4. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job, check the timestamp of objects in your Cloud Storage bucket. If there are no new files since the last run, abort the job.

Answer(s): C



You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

  1. Convert each categorical value into an integer value.
  2. Convert the categorical string data to one-hot hash buckets.
  3. Map the categorical variables into a vector of boolean values.
  4. Convert each categorical value into a run-length encoded string.

Answer(s): B



You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest, most efficient approach.
What should you do?

  1. Write a query that preprocesses the data by using BigQuery and creates a new table. Create a Vertex AI managed dataset with the new table as the data source.
  2. Use Dataflow to preprocess the data. Write the output in TFRecord format to a Cloud Storage bucket.
  3. Write a query that preprocesses the data by using BigQuery. Export the query results as CSV files, and use those files to create a Vertex AI managed dataset.
  4. Use a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library. Export the data as CSV files, and use those files to create a Vertex AI managed dataset.

Answer(s): A



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