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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 16–20 (page: 5)

Updated On: 25-Oct-2026
Page 5 of 82
PDF with 402 Questions

You work for a food product company. Your company’s historical sales data is stored in BigQuery.You need to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs min-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?

  1. Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.
  2. Write SQL queries to transform the data in-place in BigQuery.
  3. Add the transformations as a preprocessing layer in the TensorFlow models.
  4. Create a Dataflow pipeline that uses the BigQueryIO connector to ingest the data, process it, and write it back to BigQuery.

Answer(s): B



You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company’s sales data, and created a table with the following rows:
-Customer_id
-Product_id
-Date
-Days_since_last_purchase (measured in days)
-Average_purchase_frequency (measured in 1/days)
-Purchase (binary class, if customer purchased product on the Date)
You need to interpret your model’s results for each individual prediction.
What should you do?

  1. Create a BigQuery table. Use BigQuery ML to build a boosted tree classifier. Inspect the partition rules of the trees to understand how each prediction flows through the trees.
  2. Create a Vertex AI tabular dataset. Train an AutoML model to predict customer purchases. Deploy the model to a Vertex AI endpoint and enable feature attributions. Use the “explain” method to get feature attribution values for each individual prediction.
  3. Create a BigQuery table. Use BigQuery ML to build a logistic regression classification model. Use the values of the coefficients of the model to interpret the feature importance, with higher values corresponding to more importance.
  4. Create a Vertex AI tabular dataset. Train an AutoML model to predict customer purchases. Deploy the model to a Vertex AI endpoint. At each prediction, enable L1 regularization to detect non-informative features.

Answer(s): B



You work at a mobile gaming startup that creates online multiplayer games. Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated. Your model has performed well during testing, and you now need to deploy the model to production. You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue.
What should you do?

  1. Import the model into Vertex AI Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
  2. Save the model files in a Cloud Storage bucket. Create a Cloud Run function to read the model files and make online inference requests on the Cloud Run function.
  3. Save the model files in a VM. Load the model files each time there is a prediction request, and run an inference job on the VM.
  4. Import the model into Vertex AI Model Registry. Create a Vertex AI endpoint that hosts the model, and make online inference requests.

Answer(s): D



You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability.
What should you do?

  1. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.
  2. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
  3. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.
  4. Use TensorFlow I/O’s BigQuery Reader to directly read the data.

Answer(s): D



You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website. You plan to deploy the model to a new Vertex AI endpoint that will integrate with your website application. You expect higher traffic to the website during nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost.
What should you do?

  1. Configure the model deployment settings to use an n1-standard-32 machine type.
  2. Configure the model deployment settings to use an n1-standard-4 machine type. Set the minReplicaCount value to 1 and the maxReplicaCount value to 8.
  3. Configure the model deployment settings to use an n1-standard-4 machine type and a GPU accelerator. Set the minReplicaCount value to 1 and the maxReplicaCount value to 4.
  4. Configure the model deployment settings to use an n1-standard-8 machine type and a GPU accelerator.

Answer(s): B

Explanation:

This configuration allows you to optimize for both cost and performance. The n1-standard-4 machine type provides a good balance of resources for handling requests, while setting the minReplicaCount to 1 ensures that you always have at least one instance running to handle traffic, and setting the maxReplicaCount to 8 allows for scaling up during peak traffic times (nights and weekends) to minimize latency. This setup helps manage costs effectively while ensuring the application can handle higher loads when necessary.



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