PMLE Serving and Scaling Models Practice Test 10 — 15 Questions

Question 1

You are deploying a large language model on a Vertex AI endpoint. The model is loaded from a Cloud Storage bucket at container startup, which adds 3 minutes to each cold start. You want to reduce cold-start time and ensure predictable latency during scale-out. Which approach should you take?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Store the model artifacts in a custom container image and push it to Artifact Registry.. Embedding the model artifacts in a custom container image eliminates the separate download from Cloud Storage during container startup. The image layers are pulled by the node as part of standard container initialization, which is typically faster and more predictable than fetching a large model from a GCS bucket after the container starts.

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Use a larger machine type with more vCPUs and memory for each replica.

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Set the endpoint's minReplicaCount to a high value so that replicas are always warm.

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Enable request-response logging on the endpoint to monitor startup latency.

Question 2

A financial services company uses a Vertex AI Endpoint to serve a credit risk model. The model must always be available, even during maintenance windows, and they need to control the exact distribution of traffic across two model versions for a gradual rollout. They also want to minimize cold-start latency. Which deployment configuration should they use?

Accepted Answer

Deploy both model versions to the same endpoint, set minReplicaCount to at least 1 for each, and use traffic splitting to route a percentage of requests to each version.. Deploying both versions to the same endpoint with dedicated replicas and traffic splitting satisfies availability, precise rollout control, and low latency. It leverages Vertex AI's native capabilities for version management and gradual traffic shifting without additional infrastructure.

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Deploy a single model version with minReplicaCount=0 and maxReplicaCount=10, and use a custom prediction routine to handle both versions based on request headers.

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Create two separate endpoints, one for each model version, and use a global load balancer to distribute traffic equally between them.

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Deploy the model to a Vertex AI Batch Prediction job and schedule it to run every hour, then use a Cloud Function to serve predictions from the latest batch results.

Question 3

You are deploying a scikit-learn model to a Vertex AI endpoint for real-time inference. Prediction requests arrive as JSON payloads containing a single instance per request, and the model's predict method expects a pandas DataFrame with named columns. You want to avoid writing a custom container. Which approach should you take?

Accepted Answer

Upload the model artifact with a pre-built Vertex AI scikit-learn container and supply a custom prediction routine (predictor.py) that converts the decoded JSON dict into a pandas DataFrame before calling predict.. A custom prediction routine gives you a managed pre-built runtime plus a Python package that overrides preprocessing, which is the supported way to adapt request payloads to a model's expected input format. Because the estimator requires named DataFrame columns, the override must build that DataFrame explicitly; the default Predictor performs no such conversion and other pre-built containers cannot load the estimator at all.

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Upload the model artifact with a pre-built Vertex AI XGBoost container, because that container internally wraps every scikit-learn estimator and feeds it a DataFrame.

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Upload the model artifact with a pre-built TensorFlow container and wrap the scikit-learn estimator in a tf.function so the container's serving signature handles DataFrame creation.

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Upload the model artifact with a pre-built Vertex AI scikit-learn container and rely on its default Predictor, which automatically converts JSON objects into a pandas DataFrame with matching column names.

Question 4

A company runs a high-throughput inference service on a Vertex AI Endpoint backed by a custom container. During peak hours, the endpoint's CPU utilization rises to 85%, but the autoscaler does not add replicas until utilization exceeds 95%. The team wants the autoscaler to react earlier to keep latency low. They have already deployed the model and cannot change the model artifact. What should they do?

Accepted Answer

Update the endpoint's deployed model to set a lower autoscaling metric threshold, such as 70% CPU utilization.. The autoscaler on a Vertex AI Endpoint uses a target metric and threshold defined in the deployed model's autoscaling configuration. Adjusting the CPU utilization target to a lower value makes the system add replicas before latency degrades. Other approaches either do not change the scaling trigger or introduce manual processes that are slower and less reliable.

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Enable request-response logging on the endpoint and use Cloud Monitoring alerts to manually add replicas when CPU exceeds 70%.

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Increase the endpoint's minReplicaCount to match the peak traffic level so that replicas are always available.

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Redeploy the model with a larger machine type so that each replica handles more traffic and CPU never reaches the threshold.

Question 5

A retail company has deployed a scikit-learn model to a Vertex AI endpoint. The model's predictions are used to personalize the homepage. During a flash sale, the endpoint experiences a sudden 10x traffic spike, and the autoscaling configuration is set to minReplicaCount=1, maxReplicaCount=3. The endpoint becomes unresponsive. You need to modify the deployment to handle similar spikes while keeping costs low during normal hours. What should you do?

Accepted Answer

Keep minReplicaCount=1 but set maxReplicaCount to 10, and configure autoscaling based on a custom metric that tracks the number of incoming requests per second.. The best solution is to increase the maximum replica count to handle spikes and use a custom metric that directly measures request load, such as requests per second, for autoscaling. This allows rapid scaling during flash sales while keeping the minimum replicas low to control costs. CPU-based autoscaling may not react quickly enough for sudden spikes, and raising the minimum replicas increases baseline cost unnecessarily.

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Increase maxReplicaCount to 10 and configure the autoscaling metric to CPU utilization with a target of 60%.

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Deploy the model to a new endpoint with minReplicaCount=1 and maxReplicaCount=10, and use a traffic split to gradually shift traffic from the old endpoint.

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Set minReplicaCount to 3 and maxReplicaCount to 10, and enable autoscaling based on CPU utilization with a target of 80%.

Question 6

Your team has deployed a model to a Vertex AI endpoint and wants to route a small percentage of live traffic to a new model version for evaluation. You need to split traffic at the endpoint level without changing the client application. What should you do?

Accepted Answer

Deploy both model versions to the same endpoint and set a traffic split percentage.. Deploying multiple models to a single endpoint and configuring a traffic split is the native Vertex AI mechanism for canary or A/B testing. The endpoint continues to expose one URL, so clients remain unchanged, and the split percentage controls how much live traffic reaches each model version. This enables safe evaluation of the new version with minimal risk.

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Create two separate endpoints and use a load balancer to distribute traffic between them.

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Deploy the new model version as a separate endpoint and update the client to call both endpoints.

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Use a Vertex AI batch prediction job to send a percentage of live traffic to the new model.

Question 7

Your team serves a model on a Vertex AI endpoint with autoscaling. During a flash sale, traffic jumps from 50 to 900 requests per second within one minute, and many requests time out with 429 responses before new replicas become ready. You want to absorb the burst with the least user-visible impact. What should you do?

Accepted Answer

Set a higher minReplicaCount so the endpoint always keeps enough warm capacity for peak traffic, and combine it with a lower autoscaling metric target to trigger scaling earlier.. Bursts that outpace autoscaling reaction time must be met with pre-provisioned warm capacity. Setting minReplicaCount near peak demand guarantees replicas are already serving when the spike lands, and a lower metric target causes additional replicas to be requested earlier in the ramp. Merely changing the maximum, logging, or duplicating endpoints does not remove the delay between demand and available capacity.

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Increase the endpoint's maxReplicaCount and rely on the autoscaler's default metrics to add capacity as quickly as possible.

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Enable request logging on the endpoint so Cloud Logging captures the 429 responses and the autoscaler can use log volume as an additional scaling signal.

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Deploy the model to a second endpoint and split traffic between the two endpoints so each handles roughly half of the incoming requests.

Question 8

A fraud-detection model is deployed on a Vertex AI endpoint and must respond within 30 ms for 95% of requests. During testing, the team sees that p95 latency is dominated by feature retrieval from an external online store, not by model inference. They want to reduce latency without retraining the model. What should they do first?

Accepted Answer

Co-locate the feature values with the model by using a low-latency in-memory feature store or embedding the features in the request path.. The p95 latency is dominated by an external online feature store, so the serving architecture, not the model or replica count, is the limiting factor. Bringing features into the model's process via an in-memory store or passing them in the request removes the network round trip and directly attacks the dominant latency component.

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Enable request-response logging on the endpoint to identify which feature lookups are slow, then retrain the model with fewer features.

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Switch the endpoint to batch prediction mode so requests are grouped and feature lookups are amortized.

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Increase the endpoint's maxReplicaCount so more replicas can handle feature lookups in parallel.

Question 9

A company runs a Vertex AI endpoint that serves a model for real-time predictions. The endpoint uses a custom container that loads a 10 GB model into memory. During a traffic spike, the autoscaler adds new replicas, but each new replica takes 8 minutes to become ready because it must download the model from Cloud Storage. The team wants to reduce scale-up time. Which approach is most effective?

Accepted Answer

Bake the model artifacts into the custom container image and push it to Artifact Registry, then deploy the endpoint with that image.. The slow scale-up is caused by downloading a 10 GB model from Cloud Storage at container startup. By embedding the model into the container image and storing it in Artifact Registry, the model is available locally when the container starts. This removes the network transfer from the critical path and dramatically reduces the time for a new replica to become ready, allowing the autoscaler to respond faster to traffic spikes.

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Increase the minReplicaCount to a value that covers peak traffic so that new replicas are not needed.

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Store the model artifacts in a regional Cloud Storage bucket and enable parallel downloads in the custom container.

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Enable Vertex AI Model Monitoring to detect when the model is slow to load and trigger a preemptive scale-up.

Question 10

You need to run a batch prediction job on Vertex AI for a large dataset stored in BigQuery. The model expects CSV input. Which input format should you specify for the batch prediction job?

Accepted Answer

CSV files on Cloud Storage. Vertex AI batch prediction supports multiple input formats, but the format must match what the model expects. Since the model expects CSV, specifying CSV files on Cloud Storage is the correct choice. This avoids any need for data conversion and ensures the batch prediction job can parse the input correctly.

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JSON Lines files on Cloud Storage

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TFRecord files on Cloud Storage

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BigQuery table

Question 11

A financial services company deploys a fraud detection model on a Vertex AI Endpoint. The model must process each transaction in under 50 ms. The team notices that p99 latency spikes to 200 ms every few minutes. Logs show that the model container performs a cold start when new replicas are added, and the autoscaler frequently adds and removes replicas. The endpoint currently has minReplicaCount=1 and maxReplicaCount=10. What should they do to reduce the latency spikes while controlling cost?

Accepted Answer

Set minReplicaCount to a value that covers baseline traffic and increase the autoscaling cool-down period to avoid rapid scale-down.. Cold starts and rapid scale-down cause p99 latency spikes. Maintaining a baseline of warm replicas ensures that new requests do not hit a cold container, and extending the cool-down period prevents replicas from being removed too quickly. This stabilizes the replica count and keeps latency low without over-provisioning for peak traffic.

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Deploy the model to a Vertex AI Batch Prediction job and use online predictions only for high-value transactions.

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Configure the endpoint to use a smaller machine type so that replicas start faster and cold starts are shorter.

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Increase maxReplicaCount to 20 and lower the autoscaling target CPU utilization to 50%.

Question 12

A team wants to run a Vertex AI pipeline that deploys a model, runs a smoke test against the endpoint, and automatically rolls back if the smoke test fails. They need the deployment step to be reversible and the endpoint to remain available during the update. Which approach should they use?

Accepted Answer

Deploy the new model as a second deployment resource on the endpoint with 0% traffic, run the smoke test, then shift traffic and delete the old deployment if the test passes.. Adding a new deployment resource to the existing endpoint lets the team validate the new model without disrupting current traffic. Traffic can be moved only after the smoke test succeeds, and removing the old resource completes the rollout; if the test fails, the old resource is still serving and no rollback action is needed.

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Deploy the new model to a separate endpoint, run the smoke test there, and then update the application's endpoint URL after the test passes.

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Use a Vertex AI pipeline with a custom training component that trains and deploys the model in a single step, and rely on pipeline retry policies for rollback.

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Undeploy the current model, deploy the new model on the same endpoint, and run the smoke test; if it fails, redeploy the previous model.

Question 13

A retail company serves a product-ranking model on a Vertex AI endpoint. Traffic is highly predictable: a steady baseline all day with a sharp peak every evening. During the evening peak, prediction latency exceeds the SLO for several minutes before autoscaling stabilises. The team wants to reduce this scale-up lag without over-provisioning hardware for the entire day. Which configuration should they apply to the deployed model?

Accepted Answer

Configure a dedicated autoscaling metric with a lower utilization target and define a scale-up schedule aligned to the evening peak.. The latency breach is a capacity-timing problem, not a capacity-ceiling problem, so the fix must make replicas available before demand arrives. Vertex AI Model Deployment autoscaling lets you choose the metric the autoscaler tracks and set a target utilization, and it also supports scheduled scaling. Lowering the target makes scale-out begin earlier, and a schedule aligned with the predictable evening peak pre-warms replicas, eliminating the reactive ramp-up lag while keeping off-peak cost low.

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Switch the endpoint to a private endpoint and increase the machine type of each replica.

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Enable request-response logging and reduce the model's input feature count.

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Set a higher maxReplicaCount and rely on the default autoscaling metrics.

Question 14

You are deploying a model to a Vertex AI Endpoint and need to reduce inference latency for a real-time application. Which two actions should you take? (Choose two.)

Accepted Answer

Optimize the model for inference using techniques such as quantization or pruning.. Latency reduction comes from making each prediction faster, not from adding capacity. Using a GPU that fits the model's compute profile accelerates the math, and inference-specific optimizations like quantization or pruning reduce the work per prediction. Autoscaling, zero minimum replicas, and logging affect scalability, availability, or observability rather than per-request latency.

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Enable request-response logging to monitor latency metrics.

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Enable autoscaling with a high maximum replica count to handle traffic spikes.

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Set the endpoint's minReplicaCount to zero to save cost during idle periods.

Question 15

You are deploying a model to a Vertex AI endpoint that will serve predictions for a mobile application. The application sends a single request per user action and expects a response within 100 ms. The model is small and CPU-bound. You want to minimize cost while meeting the latency requirement. Which endpoint configuration should you choose?

Accepted Answer

Use a CPU-only machine type with minReplicaCount=1 and maxReplicaCount=5, and enable autoscaling based on CPU utilization.. For a small CPU-bound model with variable traffic, the optimal cost-latency trade-off is a CPU-only machine type with a low minimum replica count and autoscaling enabled. This keeps baseline cost low while allowing the endpoint to add replicas when CPU utilization increases, preserving the 100 ms latency target. GPU instances and fixed high replica counts unnecessarily increase cost without improving latency.

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Use a CPU-only machine type with minReplicaCount=5 and maxReplicaCount=5 to ensure high availability.

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Use a CPU-only machine type with minReplicaCount=1 and maxReplicaCount=1.

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Use a GPU-enabled machine type with minReplicaCount=1 and maxReplicaCount=1.