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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 96–100 (page: 21)

Updated On: 25-Oct-2026
Page 21 of 82
PDF with 402 Questions

You need to train an XGBoost model on a small dataset. Your training code requires custom dependencies. You need to set up a Vertex AI custom training job. You want to minimize the startup time of the training job while following Google-recommended practices.
What should you do?

  1. Create a custom container that includes the data and the custom dependencies. In your training application, load the data into a pandas DataFrame and train the model.
  2. Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run your training application. Create a Python source distribution that installs the custom dependencies at runtime. In your training application, read the data from Cloud Storage and train the model.
  3. Use the XGBoost prebuilt custom container. Create a Python source distribution that includes the data and installs the custom dependencies at runtime. In your training application, load the data into a pandas DataFrame and train the model.
  4. Store the data in a Cloud Storage bucket, and create a custom container with your training application and its custom dependencies. In your training application, read the data from Cloud Storage and train the model.

Answer(s): B

Explanation:

Using the prebuilt XGBoost container avoids the overhead of building and maintaining a custom container, minimizing startup time. Storing data in Cloud Storage and installing custom dependencies at runtime via a Python source distribution follows Google best practices for flexibility and efficiency in custom training jobs.



You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?

  1. Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.
  2. Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
  3. Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.
  4. Create a TensorFlow model using Google’s BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

Answer(s): B



You work for a large retailer, and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance.
What should you do?

  1. Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Use Vertex AI to evaluate the model performance.
  2. Create a logistic regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Use ML.ARIMA_EVALUATE function to evaluate the model performance.
  3. Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
  4. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.

Answer(s): D

Explanation:

Logistic regression is the appropriate choice for predicting customer churn, which is a binary classification problem. BigQuery ML supports evaluation of classification models using ML.CONFUSION_MATRIX, which provides key metrics like precision, recall, and accuracy — essential for thorough performance evaluation in this context.



You have developed a custom ML model using Vertex AI and want to deploy it for online serving. You need to optimize the model's serving performance by ensuring that the model can handle high throughput while minimizing latency. You want to use the simplest solution.
What should you do?

  1. Deploy the model to a Vertex AI endpoint resource to automatically scale the serving backend based on the throughput. Configure the endpoint's autoscaling settings to minimize latency.
  2. Implement a containerized serving solution using Cloud Run. Configure the concurrency settings to handle multiple requests simultaneously.
  3. Apply simplification techniques such as model pruning and quantization to reduce the model's size and complexity. Retrain the model using Vertex AI to improve its performance, latency, memory, and throughput.
  4. Enable request-response logging for the model hosted in Vertex AI. Use Looker Studio to analyze the logs, identify bottlenecks, and optimize the model accordingly.

Answer(s): A

Explanation:

Deploying the model to a Vertex AI endpoint leverages Google Cloud’s managed, autoscaling infrastructure, which automatically adjusts resources to meet throughput demands while minimizing latency. This is the simplest and most effective way to optimize serving performance without additional operational overhead.



You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to train a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance? (Choose two.)

  1. Increase the score threshold
  2. Decrease the score threshold.
  3. Add more positive examples to the training set
  4. Add more negative examples to the training set
  5. Reduce the maximum number of node hours for training

Answer(s): B,C



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