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 Professional-Machine-Learning-Engineer Dumps with Practice Exam Questions Answers

Questions: 285 Questions and Answers With Step-by-Step Explanation

Last Update: Jul 12, 2025

Professional-Machine-Learning-Engineer Question Includes: Single Choice Questions: 281, Multiple Choice Questions: 4,

Professional-Machine-Learning-Engineer Questions and Answers

Question # 1

You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error "Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

A.

Add a logging configuration to your application to emit logs to Cloud Logging.

B.

Change the HTTP port in your model's configuration to the default value of 8080

C.

Change the health Route value in your models configuration to /heal thcheck.

D.

Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.

Question # 2

You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?

A.

Install the NLTK library from a terminal by using the pip install nltk command.

B.

Write a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.

C.

Create a new Vertex Al Workbench notebook with a custom image that includes the NLTK library.

D.

Install the NLTK library from a Jupyter cell by using the! pip install nltk —user command.

Question # 3

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 customers1 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?

A.

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.

B.

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

C.

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

D.

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

Question # 4

Your organization’s marketing team is building a customer recommendation chatbot that uses a generative AI large language model (LLM) to provide personalized product suggestions in real time. The chatbot needs to access data from millions of customers, including purchase history, browsing behavior, and preferences. The data is stored in a Cloud SQL for PostgreSQL database. You need the chatbot response time to be less than 100ms. How should you design the system?

A.

Use BigQuery ML to fine-tune the LLM with the data in the Cloud SQL for PostgreSQL database, and access the model from BigQuery.

B.

Replicate the Cloud SQL for PostgreSQL database to AlloyDB. Configure the chatbot server to query AlloyDB.

C.

Transform relevant customer data into vector embeddings and store them in Vertex AI Search for retrieval by the LLM.

D.

Create a caching layer between the chatbot and the Cloud SQL for PostgreSQL database to store frequently accessed customer data. Configure the chatbot server to query the cache.

Question # 5

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

A.

Create alerts to monitor for skew, and retrain the model.

B.

Perform feature selection on the model, and retrain the model with fewer features

C.

Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

D.

Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

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Google Professional-Machine-Learning-Engineer Practice Exam FAQs

1. What is the Google Professional-Machine-Learning-Engineer Exam?


The Google Professional-Machine-Learning-Engineer Exam is a certification designed for professionals who build, evaluate, and optimize machine learning models using Google Cloud technologies. It assesses expertise in ML model architecture, data pipeline creation, MLOps, and responsible AI practices.

2. Who should take the Google Professional-Machine-Learning-Engineer Exam?


This exam is ideal for data scientists, ML engineers, and AI professionals who work with Google Cloud to develop scalable machine learning solutions. Candidates should have experience in Python, Cloud SQL, and distributed data processing tools.

3. What topics are covered in the Google Professional-Machine-Learning-Engineer Exam?


The Google Professional-Machine-Learning-Engineer exam covers:

  • ML model development using BigQuery ML
  • Data pipeline creation and feature engineering
  • MLOps principles for model deployment and monitoring
  • Generative AI solutions using Vertex AI
  • Responsible AI practices and fairness in ML models

4. What is the format of the Google Professional-Machine-Learning-Engineer Exam?


The Google Professional-Machine-Learning-Engineer exam consists of multiple-choice and multiple-select questions. It does not directly assess coding skills, but candidates should be able to interpret Python and Cloud SQL code snippets.

5. Is Professional-Machine-Learning-Engineer certification worth it?


Yes, the Google Professional-Machine-Learning-Engineer certification is highly valuable for professionals in machine learning and AI. It enhances credibility, improves job prospects, and ensures familiarity with industry best practices.

6. How to be a Professional-Machine-Learning-Engineer?


To become a Professional Machine Learning Engineer, follow these steps:

  • Learn the Fundamentals – Gain expertise in Python, statistics, and ML algorithms.
  • Master Google Cloud ML Tools – Study Vertex AI, TensorFlow, and BigQuery ML.
  • Build ML Models – Work on real-world projects to develop and optimize ML models.
  • Understand MLOps – Learn model deployment, monitoring, and automation.
  • Prepare for the Certification Exam – Use Google Cloud’s official resources and Professional-Machine-Learning-Engineer practice questions from Dumpstool.

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The certification is valid for two years, after which candidates must recertify to maintain their credentials.

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