PMLE Practice Test 25 — 15 Questions

Question 1

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 2

A company deploys a custom TensorFlow model to Vertex AI Endpoint for online predictions. After deployment, prediction latency is consistently high (over 500ms) even under low traffic. The model is CPU-only and the default machine type (n1-standard-2) is used. Which action will most likely reduce prediction latency?

Accepted Answer

Change the machine type to n1-highcpu-16 with a GPU accelerator.. Option A is correct because using a machine type with more CPUs or adding a GPU accelerator can reduce inference time for compute-intensive models. Option B is wrong because increasing max replicas does not improve single-request latency. Option C is wrong because batch size affects throughput, not latency per request. Option D is wrong because increasing min replicas reduces cold start but not steady-state latency.

Answer

Increase the max_replica_count to 10 to allow more parallel requests.

Answer

Set min_replica_count to 3 to ensure always-on capacity.

Answer

Increase the batch size in the prediction request.

Question 3

Which TWO actions can help reduce prediction latency for a model deployed on Vertex AI Endpoint without changing the model architecture?

Accepted Answer

Attach a GPU accelerator to the endpoint's machine type.. Options A and D are correct. Option A (GPU accelerator) can significantly speed up inference for deep learning models. Option D (model quantization) reduces model size and inference time. Option B (increasing batch size) increases latency per request. Option C (multiregion deployment) reduces network latency but not prediction latency. Option E (smaller machine type) may increase latency.

Answer

Increase the batch size of prediction requests.

Answer

Deploy the model in multiple regions and use global load balancing.

Answer

Use a smaller machine type to reduce complexity.

Question 4

A company is serving a model for their e-commerce website. They expect traffic to be low at night and very high during flash sales. They want to minimize costs while ensuring availability during spikes. Which autoscaling configuration should they use?

Accepted Answer

min_replica_count=1, max_replica_count=20, target_cpu=60. Setting a high max_replica_count allows scaling to handle spikes, while a low min_replica_count saves cost during low traffic. CPU utilization target of 60% is reasonable.

Answer

min_replica_count=5, max_replica_count=5, target_cpu=60

Answer

min_replica_count=10, max_replica_count=10, target_cpu=60

Answer

min_replica_count=0, max_replica_count=100, target_cpu=80

Question 5

Which THREE factors should you consider when deciding between online prediction and batch prediction on Vertex AI?

Accepted Answer

Cost per prediction: batch is often cheaper per request. Latency requirements, cost structure, and data volume patterns are key factors. Instance availability is similar for both; model architecture does not dictate prediction type.

Answer

The type of machine learning model architecture (e.g., CNN vs RNN)

Answer

Availability of GPU instances in the region

Question 6

A company has deployed a TensorFlow model on Vertex AI Prediction for real-time inference. They notice that during peak hours, the prediction latency increases significantly, and some requests time out. The model requires GPU acceleration. Which action should they take to reduce latency and avoid timeouts?

Accepted Answer

Enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, and ensure GPU quota is sufficient.. Option A is correct because enabling autoscaling with appropriate min and max replicas dynamically adjusts capacity to handle peak load, and ensuring sufficient GPU quota prevents resource constraints. Option B is wrong because statically increasing replicas leads to resource waste during low traffic and may not react quickly to spikes. Option C is wrong because increasing CPU resources does not address GPU-bound inference. Option D is wrong because Cloud Functions is not designed for GPU-accelerated inference and introduces additional latency.

Answer

Switch to a larger machine type with more vCPUs.

Answer

Increase the number of replicas in the Vertex AI Prediction endpoint statically to handle peak load.

Answer

Use Cloud Functions to invoke the model asynchronously.

Question 7

A company runs batch predictions on a large dataset using Vertex AI Batch Prediction. They want to reduce costs without significantly increasing processing time. Which three actions should they take? (Choose three.)

Accepted Answer

Use preemptible VMs for the batch prediction job.. Options A, C, and E are correct. A uses preemptible VMs which are cheaper. C tunes batch size to maximize throughput per worker. E uses custom machine types to avoid overprovisioning. Option B increases machine size which may increase cost per worker. Option D uses TPUs which are more expensive and may not be beneficial for all model types.

Answer

Use a larger machine type to reduce the number of workers.

Answer

Use TPUs instead of GPUs to accelerate processing.

Question 8

A team needs to serve a PyTorch model for production inference with strict latency requirements (p99 < 100ms). The model has dynamic control flow and uses custom kernels compiled with torch.jit. Which serving approach should they recommend?

Accepted Answer

Build a custom container with PyTorch JIT and deploy it on Vertex AI Prediction.. Option C is correct because a custom container with a PyTorch JIT server offers full control over the model execution and avoids overhead of generic servers. Option A is wrong because Vertex AI Prediction does not support custom containers? Actually it does, but the best fit for dynamic control flow is a custom container. Option B is wrong because TensorFlow Serving does not support PyTorch natively. Option D is wrong because Cloud Functions are not suitable for real-time inference at scale with strict latency.

Answer

Convert the model to TensorFlow SavedModel and serve it on Vertex AI Prediction with TensorFlow Serving.

Answer

Use Cloud Functions with a PyTorch wrapper to handle inference requests.

Answer

Deploy the model on Vertex AI Prediction using the prebuilt PyTorch container.

Question 9

A data science team deploys a large language model (LLM) on Vertex AI Prediction using an NVIDIA A100 GPU. The end-to-end latency is acceptable, but the cost is high due to low GPU utilization. The model is stateless and requests are independent. Which strategy would most effectively reduce cost per prediction?

Accepted Answer

Implement dynamic batching in the serving container to aggregate multiple requests into a single inference call.. Option A is correct because request batching increases throughput per GPU, reducing cost per prediction. Option B is wrong because a smaller GPU may not meet latency requirements. Option C is wrong because Cloud TPUs are not designed for this model and may increase cost. Option D is wrong because scaling down replicas reduces capacity and may cause latency spikes.

Answer

Migrate the model to Cloud TPU using TensorFlow to benefit from higher throughput.

Answer

Use a smaller GPU, such as NVIDIA T4, and increase the number of replicas to maintain throughput.

Answer

Reduce the number of min replicas to 0 and scale from 0 on each request.

Question 10

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 11

Which THREE factors should be considered when choosing between using Vertex AI Endpoints and Cloud Run for model serving? (Choose three.)

Accepted Answer

Built-in model monitoring. Options A, B, and C are key differentiators. Vertex AI Endpoints supports GPUs natively, Cloud Run has limited GPU support. Cloud Run inherently scales to zero, Vertex AI endpoints don't always scale to zero easily. Vertex AI Endpoints has built-in model monitoring, Cloud Run does not. Options D and E are less differentiating: both services have similar cost structures and container requirements.

Answer

Complexity of model containerization

Answer

Cost per request

Question 12

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 13

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 14

Refer to the exhibit. A data scientist deploys a new model version (model_v2) to an existing endpoint with 20% traffic. After a few days, they notice that model_v2's error rate is higher than model_v1's. They want to route all traffic back to model_v1 immediately. Which command achieves this with minimal disruption?

Accepted Answer

gcloud ai endpoints update my-endpoint --region=us-central1 --traffic-split=model_v1=1,model_v2=0. Option A is correct. The gcloud ai endpoints update command with --traffic-split allows setting the traffic split to 100% for model_v1 and 0% for model_v2, routing all traffic to the stable model without redeploying. Option B removes the model, which may cause temporary unavailability. Option C uses a misspelled command. Option D changes the endpoint's update time but not traffic.

Answer

gcloud ai endpoints update my-endpoint --region=us-central1 --remove-deployed-model=model_v2

Answer

gcloud ai endpoints undepoly-model my-endpoint --region=us-central1 --model=model_v2

Answer

gcloud ai endpoints update-traffic my-endpoint --region=us-central1 --model=model_v1 --traffic-percentage=100

Question 15

A data science team deploys a custom container on Vertex AI Prediction for a PyTorch model. After deployment, the model returns predictions that are consistently off by a constant factor. The model performed correctly during local testing. What is the most likely cause?

Accepted Answer

The serving input function in the container is not applying the same normalization as during training.. Option B is correct because a constant factor error typically indicates a preprocessing mismatch, such as different normalization. Option A is wrong because different PyTorch versions may cause other inconsistencies but not a constant factor. Option C is wrong because training vs evaluation mode affects dropout/batch norm, not constant scaling. Option D is possible but less specific than preprocessing.

Answer

The model is loaded in evaluation mode, but the training mode was used in testing.

Answer

The container is using a different PyTorch version than the training environment.

Answer

There is a bug in the custom container's prediction route.