support@Free-Braindumps.com
Register Login
Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
CrowdStrike
Anthropic
All Vendors
  1. Home
  2. Exams
  3. Google
  4. PROFESSIONAL MACHINE LEARNING ENGINEER

Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 141–145 (page: 30)

Updated On: 25-Oct-2026
Page 30 of 82
PDF with 402 Questions

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers’ account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

  1. 1. Create a Pub/Sub topic for each user.
    2. Deploy a Cloud Function that sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold.
  2. 1. Create a Pub/Sub topic for each user.
    2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold.
  3. 1. Build a notification system on Firebase.
    2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.
  4. 1. Build a notification system on Firebase.
    2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold.

Answer(s): D



You work for a bank. You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex AI services to include in the workflow. You want to track the model’s training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics.
Which Vertex AI services should you use?

  1. Vertex ML Metadata, Vertex AI Feature Store, and Vertex AI Vizier
  2. Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Vizier
  3. Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard
  4. Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI TensorBoard

Answer(s): C



You are developing a training pipeline for a new XGBoost classification model based on tabular data. The data is stored in a BigQuery table. You need to complete the following steps:
1. Randomly split the data into training and evaluation datasets in a 65/35 ratio
2. Conduct feature engineering
3. Obtain metrics for the evaluation dataset
4. Compare models trained in different pipeline executions
How should you execute these steps?

  1. 1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering.
    2. Enable autologging of metrics in the training component.
    3. Compare pipeline runs in Vertex AI Experiments.
  2. 1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering.
    2. Enable autologging of metrics in the training component.
    3. Compare models using the artifacts’ lineage in Vertex ML Metadata.
  3. 1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits.
    2. Use a SQL view to apply feature engineering and train the model using the data in that view.
    3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.
  4. 1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits.
    2. Use ML TRANSFORM to specify the feature engineering transformations and tram the model using the data in the table.
    3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.

Answer(s): A



You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible.
What should you do?

  1. Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
  2. Load the model directly into the Dataflow job as a dependency, and use it for prediction.
  3. Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
  4. Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.

Answer(s): C



You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management.
What approach should you use?

  1. Use Kubeflow Pipelines on Google Kubernetes Engine.
  2. Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
  3. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
  4. Use Cloud Composer for the orchestration.

Answer(s): C



  PDF
Viewing page 30 of 82
Viewing questions 146 - 150 out of 402 questions


Post your Comments and Discuss Google PROFESSIONAL MACHINE LEARNING ENGINEER exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!