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Google Professional Data Engineer Practice Test

499 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

This is exactly what you see during practice — question, options, and a full explanation after you answer.

Q1Operationalizing machine learning modelsmedium
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A company deploys a machine learning model to Vertex AI for real-time predictions. After deployment, they notice that prediction latency spikes during peak traffic hours. Which approach should they take to reduce latency without sacrificing accuracy?

Configure auto-scaling with higher min and max instancesCorrect
BReduce the number of input features
CSwitch from online to batch prediction
DUse a larger machine type for the model

Option A is correct because configuring auto-scaling with higher min and max instances ensures that Vertex AI has sufficient pre-warmed replicas to handle traffic spikes without cold-start latency. This approach maintains model accuracy because it does not alter the model archite…Read full explanation

Q2Operationalizing machine learning modelshard
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A data science team uses Vertex AI Pipelines to automate retraining. They want to ensure that only models with performance above a threshold are deployed. Which component should they add to the pipeline?

AVertex AI Feature Store
Vertex AI Model EvaluationCorrect
CCloud Build trigger
DCloud Monitoring alert

Vertex AI Model Evaluation provides built-in evaluation metrics and threshold-based validation that can be used as a pipeline condition to gate model deployment. By adding a Model Evaluation component, the pipeline can compare model performance against a predefined threshold and …Read full explanation

Q3Operationalizing machine learning modelseasy
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A company trains a custom model using TensorFlow and wants to deploy it to Vertex AI for low-latency predictions. The model is large (2 GB). Which deployment option should they choose?

AUse Vertex AI Batch Prediction job
BDeploy as a Cloud Function
Deploy to Vertex AI Endpoint with a custom containerCorrect
DDeploy to Cloud Run with minimum instances

Option C is correct because deploying a large (2 GB) model to Vertex AI Endpoint with a custom container allows you to package the model, its dependencies, and a serving framework (e.g., TensorFlow Serving) into a Docker image. This approach supports low-latency predictions by ke…Read full explanation

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