PMLE Serving and scaling models Practice Test 3 — 15 Questions

Question 1

A team wants to deploy two versions of a model (v1 and v2) on Vertex AI Endpoint to conduct an A/B test. They need to split traffic so that 10% of requests go to v2. Which configuration achieves this?

Accepted Answer

Deploy both versions on the same endpoint and use the `traffic_split` parameter to allocate 90% to v1 and 10% to v2.. Option C is correct because Vertex AI Endpoints support traffic splitting by allocating percentages to each model. Option A is wrong because canary deployment gradually rolls out, not fixed split. Option B is wrong because multiple endpoints cannot share traffic splitting. Option D is wrong because routing at load balancer is not necessary.

Answer

Configure a global load balancer in front of two endpoints and set the weight.

Answer

Create two separate endpoints, one for each version, and have the client randomly select the endpoint.

Answer

Deploy v2 as a canary deployment and set the canary rollout to 10% in Cloud Deployment Manager.

Question 2

A model serving team notices that during a flash sale, a real-time recommendation model experiences sudden spikes in traffic, causing some requests to time out. The endpoint is configured with `min_replica_count=3`, `max_replica_count=10`, and autoscaling metric set to `target_utilization=0.6` on CPU. Despite this, autoscaling is too slow. What change will most improve the autoscaling responsiveness?

Accepted Answer

Change the autoscaling metric to 'average request count per replica' with an appropriate target.. Option A is correct because using request count per replica (transactions per second) as a direct measure of load triggers autoscaling faster. Option B is wrong because increasing target utilization makes it slower. Option C is wrong because GPU metrics are only relevant for GPU models. Option D is wrong because reducing min replicas may cause underprovisioning.

Answer

Add a custom metric based on GPU utilization, assuming the model uses GPU.

Answer

Increase the target CPU utilization to 0.8 to reduce the number of replicas and save cost.

Answer

Reduce `min_replica_count` to 1 to allow more aggressive scaling.

Question 3

A machine learning engineer wants to manage multiple model versions and facilitate collaboration across teams. The goal is to track model lineage, versioning, and approvals. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Registry. Option C is correct because Model Registry provides versioning, approval tracking, and integration with Vertex AI Pipelines. Option A is wrong because Feature Store stores features, not models. Option B is wrong because ML Metadata is lower-level and less user-friendly. Option D is wrong because Vizier is for hyperparameter tuning.

Answer

Vertex AI ML Metadata

Answer

Vertex AI Feature Store

Answer

Vertex AI Vizier

Question 4

A company needs to serve a model for low-frequency inference requests (a few hundred per month) from multiple regions. The priority is simplicity and minimal cost without maintaining infrastructure. Which serving option should they choose?

Accepted Answer

Use Vertex AI Batch Prediction triggered as needed.. Option D is correct because Vertex AI Batch Prediction runs on demand and is cost-effective for infrequent large batches. Option A is wrong because real-time endpoint incurs per-hour cost even if idle. Option B is wrong because Cloud Run is better for online, not offline. Option C is wrong because Dataflow is more complex and designed for streaming.

Answer

Deploy a real-time Vertex AI Endpoint with min replicas set to 1.

Answer

Set up a Dataflow streaming pipeline to process requests.

Answer

Use Cloud Run with serving container and scale to zero.

Question 5

A team deploys a real-time model using a custom container on Vertex AI Prediction. The container is large (5 GB) and cold starts are causing latency spikes. The endpoint is configured with `min_replica_count=0` to reduce cost. The team wants to keep the cost low while reducing cold starts. What is the best approach?

Accepted Answer

Set `min_replica_count=1` to keep at least one replica always warm.. Option B is correct because configuring a minimum number of always-on replicas (e.g., 1) eliminates cold starts for most traffic. Option A is wrong because it may not help if container is large. Option C is wrong because prebuilding images doesn't reduce cold start startup overhead. Option D is wrong because SSD can help but not eliminate cold start latency.

Answer

Use a prebuilt container for the model framework to reduce image size.

Answer

Enable container memory optimization to reduce startup time.

Answer

Provision a Persistent Disk (SSD) for the container image to speed up download.

Question 6

Which TWO are best practices for deploying models to Vertex AI Prediction? (Choose 2.)

Accepted Answer

Monitor prediction latency and error rates with Cloud Monitoring alerts.. Options B and D are correct. Option A is wrong because exact same environment may not be available. Option C is wrong because version aliases should be used for easy rollback. Option E is wrong because logging all inputs may cause privacy issues.

Answer

Log all raw prediction inputs and outputs for every request for auditing.

Answer

Always deploy the model in the same environment as training to avoid incompatibility.

Answer

Use the default model version alias 'default' for all deployments to simplify updates.

Question 7

A model serving team is experiencing high latency in production. Which TWO actions should they take to diagnose the root cause? (Choose 2.)

Accepted Answer

Enable Cloud Trace to analyze request latency across services.. Options A and D are correct. Option B is wrong because increasing replicas may mask the issue but not diagnose. Option C is wrong because converting framework may not address latency. Option E is wrong because log level changes do not provide granular latency analysis.

Answer

Convert the model to a different framework that is faster.

Answer

Increase the number of replicas to reduce load per replica.

Answer

Set the logging verbosity to DEBUG in the container.

Question 8

Which THREE factors are critical when designing a model serving architecture for a global user base with strict latency SLAs? (Choose 3.)

Accepted Answer

Enable autoscaling with request-based metrics to handle traffic spikes.. Options B, C, and E are correct. Option A is wrong because single-region deployment cannot meet global latency. Option D is wrong because batch processing adds latency.

Answer

Use batch prediction to process requests in bulk for efficiency.

Answer

Deploy the model in a single region to avoid data sovereignty issues.

Question 9

A company deploys a model on Vertex AI Endpoints for real-time inference. They notice latency spikes during peak hours. Which action is most effective to reduce latency without sacrificing accuracy?

Accepted Answer

Enable autoscaling based on CPU utilization. Option B is correct because enabling autoscaling based on CPU utilization dynamically adjusts the number of instances to handle traffic spikes, reducing latency. Option A increases cost without addressing scaling elasticity. Option C may help but not during peaks if requests are unique. Option D can reduce accuracy.

Answer

Use a larger machine type

Answer

Reduce model size by pruning

Answer

Implement client-side caching

Question 10

A team uses Vertex AI Prediction with a custom container. They want to perform canary deployments by sending 5% of traffic to a new model version. Which method should they use?

Accepted Answer

Deploy two separate endpoints and use a load balancer. Option C is correct because Vertex AI endpoints support traffic splitting between deployed models, allowing a controlled canary rollout. Option A is not possible as endpoints cannot have separate traffic splits on different deployments without manual configuration. Option B is incorrect as Model Registry itself does not handle traffic splitting. Option D uses Cloud Run which is not integrated with Vertex AI Prediction.

Answer

Create a new endpoint with manual traffic splitting

Answer

Use Cloud Run for serving with gradual rollout

Answer

Use the Vertex AI Model Registry and configure traffic splitting on the endpoint

Question 11

A machine learning engineer notices that a model served on Vertex AI Endpoints returns predictions that are consistently 20% slower during the first request after idle (cold start). They are using automatic scaling with min replicas=1. What is the most likely cause and best solution?

Accepted Answer

Vertex AI endpoint warm-up time; set min replicas to 2 to always keep a warm instance. Option D is correct because even with min replicas=1, an instance may be recycled due to idleness or updates, causing a cold start. Setting a higher min replicas (e.g., 2) ensures a warm instance is always available. Option A is incorrect because smaller containers may not significantly reduce cold start. Option B is incorrect as health checks measure readiness but do not eliminate cold starts. Option C is incorrect as preemptible instances are not relevant to cold starts.

Answer

Model loading time is high; enable health checks to warm the instance

Answer

Network latency; deploy to a different region

Answer

Container initialization delay; use a smaller container image

Question 12

An organization needs to serve a large model (10 GB) with low latency across multiple regions. Which Vertex AI feature best meets this requirement?

Accepted Answer

Global endpoints. Option A is correct because Vertex AI Global Endpoints automatically route traffic to the nearest region with capacity, reducing latency for geographically distributed users. Option B is for batch jobs, not real-time. Option C is for private access within VPC, which does not address multi-region latency. Option D is for monitoring, not serving.

Answer

Private endpoints

Answer

Batch prediction

Answer

Model Monitoring

Question 13

A data scientist uses Vertex AI Workbench to train a model and then deploys it to an endpoint. They want to automate the retraining and redeployment pipeline when new data arrives. Which service should they use?

Accepted Answer

Vertex AI Pipelines. Option C is correct because Vertex AI Pipelines provides a serverless, managed pipeline orchestration service that can automate retraining and redeployment. Option A (Cloud Composer) is a workflow orchestration service but is more complex and not as integrated with Vertex AI. Option B (Cloud Functions) is event-driven but lacks pipeline capabilities. Option D (Cloud Scheduler) is for scheduled jobs, not event-driven retraining.

Answer

Cloud Composer

Answer

Cloud Scheduler

Answer

Cloud Functions

Question 14

A model deployed on Vertex AI Endpoints returns predictions, but the performance metrics (e.g., AUC) degrade over time. The input data distribution is shifting. The team wants to detect and alert on this drift automatically. Which set of actions should they take?

Accepted Answer

Enable Vertex AI Model Monitoring for feature drift and set up alerts via Cloud Monitoring. Option B is correct because Vertex AI Model Monitoring can monitor for feature distribution drift and skew, and can be configured to send alerts via Cloud Monitoring. Option A is part of model interpretation, not drift detection. Option C requires ground truth labels, which may not be available immediately. Option D is manual and not automated.

Answer

Schedule a batch prediction job daily and compare with ground truth

Answer

Use Vertex AI Explainable AI to understand predictions

Answer

Implement custom logging in the serving container and use BigQuery for analysis

Question 15

For a low-latency real-time serving requirement, which type of Vertex AI Endpoint is appropriate?

Accepted Answer

Regional endpoint. Option C is correct because a regional endpoint can be deployed in the same region as the clients to minimize network latency, and it provides low-latency serving. Option A (private endpoint) is for security, not necessarily low latency. Option B (public endpoint) adds internet latency. Option D (global endpoint) is optimized for multi-region traffic but may add slight overhead.

Answer

Public endpoint

Answer

Private endpoint with VPC network

Answer

Global endpoint