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Google Professional Machine Learning Engineer Practice Test

506 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

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Full explanations included
Domain score breakdown
Real exam: 120 min
Pass mark: 720%

Sample questions with explanations

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Q1Architecting low-code ML solutionsmedium
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A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?

AUse a pre-built recommendation model from Vertex AI Model Garden and deploy it to an endpoint.
BWrite a custom TensorFlow model using the Vertex AI Training service and deploy it via Vertex AI Prediction.
CExport the data to CSV and use AutoML Tables to train a recommendation model.
Use BigQuery ML's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.Correct

Option D is correct because BigQuery ML's matrix factorization model (model_type='matrix_factorization') is purpose-built for recommendation systems using implicit or explicit feedback data. It trains directly on historical interaction data (e.g., user-item purchases) without req…Read full explanation

Q2Architecting low-code ML solutionseasy
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A data scientist wants to quickly train a binary classification model on a tabular dataset stored in BigQuery without writing any code. They have limited ML experience. Which Google Cloud service should they use?

AVertex AI Workbench with a built-in scikit-learn notebook.
BDataflow with a TensorFlow pipeline.
BigQuery ML with CREATE MODEL statement using SQL.Correct
DAutoML Tables with a direct BigQuery connection.

Option C is correct because BigQuery ML allows a data scientist to train a binary classification model directly in BigQuery using a `CREATE MODEL` SQL statement, without writing any code or moving data. This is the fastest low-code approach for users with limited ML experience, a…Read full explanation

Q3Architecting low-code ML solutionshard
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A company uses Vertex AI Pipelines to orchestrate their ML training workflow. The pipeline includes a BigQuery ML training step, a model evaluation step, and a deployment step to Vertex AI Endpoints. The engineer notices that the pipeline fails intermittently due to a quota exceeded error on Vertex AI Endpoints during model deployment. What is the best long-term solution to prevent this failure?

ARun the pipeline steps sequentially with longer wait times.
Add retry logic with exponential backoff to the deployment step in the pipeline.Correct
CSwitch to deploying models using a custom container on Compute Engine.
DRequest a permanent quota increase for Vertex AI Endpoints.

Option D is correct because implementing retry logic with exponential backoff is a resilient pattern for transient quota errors. Option A is wrong because increasing quota requires a support ticket and may not be granted immediately. Option B is wrong because using a custom conta…Read full explanation

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