20+ practice questions focused on Serving and scaling models — one of the most tested topics on the Google Professional Machine Learning Engineer exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Serving and scaling models PracticeA company deploys a TensorFlow model on Vertex AI Prediction with a single node. During peak hours, inference latency increases. What should they do first to reduce latency?
Explanation: Enabling autoscaling for the deployment is the correct first step because it allows Vertex AI Prediction to dynamically adjust the number of replicas based on incoming traffic. During peak hours, autoscaling can add more nodes to distribute the inference load, directly reducing latency without requiring manual intervention or over-provisioning.
A data science team deploys a PyTorch model using Vertex AI Prediction. The model requires GPU for inference, but they notice high costs and underutilized GPUs during off-peak hours. What is the most cost-effective solution?
Explanation: Option C is correct because setting min replicas to 0 allows Vertex AI Prediction to scale down to zero instances during off-peak hours, eliminating GPU costs when no requests are being served. Combined with autoscaling, the deployment will spin up GPU-backed instances on demand only when traffic arrives, directly addressing the underutilization issue while maintaining low latency for inference requests.
A company serves a scikit-learn model on Vertex AI Prediction but receives a 400 error with 'Prediction failed: Model evaluation error'. What is the most likely cause?
Explanation: Vertex AI Prediction supports specific versions of scikit-learn for serving models. If the model was trained with a version that is not in the supported list (e.g., 0.19, 0.20, 0.22, 0.23, 0.24, 1.0, 1.1), the prediction endpoint will fail with a 'Model evaluation error' because the underlying runtime cannot load the serialized model (e.g., pickle or joblib file). This is the most likely cause of a 400 error when the input format is otherwise correct.
A company wants to serve a large XGBoost model that exceeds the 2GB limit for Vertex AI Prediction. What should they do?
Explanation: Vertex AI Prediction has a 2GB limit for the model artifact when using pre-built containers. A custom container bypasses this limit because you package the model and serving code into a Docker image, which can be arbitrarily large. This allows you to serve XGBoost models exceeding 2GB without size constraints imposed by the managed serving infrastructure.
A company deploys a model on Vertex AI Prediction with autoscaling enabled. They notice that during a traffic spike, new instances take several minutes to become available, causing high latency. What is the best solution?
Explanation: Option D is correct because setting a higher min replicas ensures that a baseline number of instances are always warm and ready to serve traffic. During a traffic spike, new instances still take time to provision (cold start), but the warm instances handle the initial surge without latency spikes. This directly addresses the observed high latency during spikes.
+15 more Serving and scaling models questions available
Practice all Serving and scaling models questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Serving and scaling models. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Serving and scaling models questions on the PMLE frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Serving and scaling models is tested as part of the Google Professional Machine Learning Engineer blueprint. Practicing with targeted Serving and scaling models questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Serving and scaling models is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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