PMLE Practice Test 26 — 15 Questions

Question 1

A company deploys a model on Vertex AI Prediction for real-time inference. Users report intermittent high latency during peak hours. The model is deployed on a single machine type with `min_replica_count=1` and `max_replica_count=5`. Autoscaling is enabled based on CPU utilization. What is the most likely cause of the latency spikes?

Accepted Answer

Autoscaling based on CPU utilization does not react quickly to inference request spikes.. Option B is correct because CPU utilization may not be a good proxy for inference load; the system may not scale up fast enough under sudden traffic bursts. Option A is wrong because Vertex AI automatically manages container health. Option C is wrong because Vertex AI endpoints automatically distribute traffic. Option D is wrong because the container image is built correctly.

Answer

The model server is crashing under load due to memory issues.

Answer

The load balancer is misconfigured and routes traffic unevenly.

Answer

The container image is not optimized for the model.

Question 2

A company has deployed a fraud detection model on Vertex AI Prediction. After three months, the model's accuracy has degraded, and the business is losing money due to undetected fraud. What should the team implement to proactively detect such issues?

Accepted Answer

Enable Vertex AI Model Monitoring to track prediction drift and alert when metrics exceed thresholds.. Option B is correct because monitoring prediction drift is a key practice for model quality. Option A is wrong because logs don't automatically detect drift. Option C is wrong because model monitoring helps, but retraining alone doesn't detect. Option D is wrong because shuffling data doesn't address drift.

Answer

Set up Cloud Logging to capture all prediction requests and responses for manual review.

Answer

Randomly shuffle the training data before retraining to improve robustness.

Answer

Schedule a monthly job to retrain the model with the latest data without monitoring.

Question 3

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 4

Which TWO options are best practices for reducing model serving latency on Vertex AI Endpoints? (Choose two.)

Accepted Answer

Optimize the model using quantization or pruning. Options C and E are correct. Deploying in the same region as clients reduces network latency. Optimizing the model (quantization/pruning) reduces compute time without major accuracy loss. Option A increases cost but not necessarily latency. Option B is not a feature. Option D increases latency due to batch processing.

Answer

Use a larger machine type with more memory

Answer

Use batch prediction instead of online prediction

Answer

Enable model caching at the endpoint

Question 5

A data scientist runs a batch prediction job on Vertex AI using a custom container. The job processes a large JSONL file (10 GB) and fails with an out-of-memory error. The machine type is n1-standard-4 (15 GB memory). Which action should be taken to resolve the error while minimizing cost?

Accepted Answer

Use a machine type with more memory, such as n1-highmem-8 (52 GB).. Option C is correct because out-of-memory errors suggest the machine's memory is insufficient for the model or data size; increasing to a high-memory machine type adds more memory. Option A is wrong because splitting input data does not reduce per-instance memory pressure if the model itself is large. Option B is wrong because the batch size may need adjustment but the primary issue is memory. Option D is wrong because using a GPU does not increase memory.

Answer

Reduce the batch size in the prediction request.

Answer

Split the input data into smaller files and run multiple batch jobs.

Answer

Add a GPU accelerator to offload computation.

Question 6

A machine learning team wants to perform A/B testing between two model versions (v1 and v2) on Vertex AI Endpoint. They need to gradually route 10% of traffic to v2 while monitoring performance. What is the most efficient way to achieve this?

Accepted Answer

Deploy both versions to the same endpoint and set traffic_split to 90% for v1 and 10% for v2.. Option B is correct because Vertex AI Endpoint natively supports traffic splitting between model versions. Option A is wrong because creating separate endpoints adds complexity and cost. Option C is wrong because Cloud Load Balancing operates at the network level, not model level. Option D is wrong because batch prediction is not for real-time A/B testing.

Answer

Use a Cloud Load Balancer to route traffic based on a header.

Answer

Create two separate endpoints and use a weighted DNS round-robin.

Answer

Run batch predictions for v2 and log results separately.

Question 7

A company deploys a model to Vertex AI Endpoint with autoscaling enabled. During a traffic spike, they observe high tail latency (99th percentile > 2s). Which TWO factors are most likely contributing to this latency?

Accepted Answer

The min_replica_count is set too low, causing cold starts.. Options A and C are correct. Option A: if min replicas is too low, new replicas must be created and loaded with the model, causing cold start latency. Option C: a large model file increases cold start time as new replicas load the model. Option B (underpowered machine) would cause high average latency, not just tail. Option D (too many traffic splits) is unrelated. Option E (target CPU utilization set too low) would cause earlier scaling, reducing tail latency; too high would delay scaling.

Answer

The machine type is underpowered for the model.

Answer

The autoscaling target_cpu_utilization is set too low (e.g., 0.3).

Answer

The endpoint has too many traffic splits configured.

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 uses Vertex AI Endpoints for model serving and wants to implement A/B testing between model versions. They need to gradually shift traffic from the old to the new version while monitoring performance. Which Vertex AI feature allows this with minimal operational overhead?

Answer

Using a custom load balancer with weighted backend services

Answer

Model Deployments with traffic splitting

Answer

Vertex AI Experiments for tracking

Answer

Cloud Run revisions with traffic migration

Question 10

A company needs to serve a model for real-time predictions with a strict latency SLA of 100ms at the 99th percentile. The model is lightweight and traffic patterns are highly variable with occasional spikes. Which deployment strategy best meets the SLA while controlling cost?

Accepted Answer

Deploy to Vertex AI Endpoint with min_replica_count=3 and autoscaling enabled.. Option D is correct because setting a minimum number of replicas ensures baseline capacity to handle initial spikes without cold start delays, while autoscaling handles larger spikes. Option A is wrong because batch prediction is not real-time. Option B is wrong because no scaling may cause over-provisioning or under-provisioning. Option C is wrong because Cloud Run with no accelerator may not meet latency SLA for ML models.

Answer

Deploy the model as a Cloud Run service with autoscaling to zero.

Answer

Deploy to Vertex AI Endpoint with manual scaling and a fixed number of replicas.

Answer

Use Vertex AI Batch Prediction.

Question 11

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 12

Your team is serving a large language model on Vertex AI using a custom container. The endpoint experiences intermittent 502 errors during traffic spikes. The autoscaling configuration uses a CPU utilization target of 60% and the model is deployed on n1-standard-4 instances. The model requires significant memory. Which combination of changes is most likely to resolve the issue?

Accepted Answer

Switch to a machine type with more memory, e.g., n1-highmem-8, and increase min_replica_count.. The 502 errors likely indicate the instances are overwhelmed or timing out. Increasing the machine type to a high-memory instance reduces memory pressure, and adding more replicas through a lower target scaling metric or higher min replicas provides capacity. Tuning batch size helps but is secondary. GPU may not help if the issue is memory.

Answer

Increase the target CPU utilization to 90% to allow more requests per instance.

Answer

Enable canary traffic splitting to reduce load on the main endpoint.

Answer

Reduce the model batch size from 32 to 1 to lower memory per request.

Question 13

You are using Vertex AI continuous evaluation (model monitoring) for your deployed model. You receive an alert that the prediction distribution is significantly different from the training distribution. What should you do first?

Accepted Answer

Analyze the input data to understand if there is a skew or drift.. When a monitoring alert triggers, the first step is to investigate the root cause: check if input data has changed, retraining is needed, or there is a data pipeline issue. Simply rolling back or retraining without analysis might be premature.

Answer

Roll back the model to the previous version immediately.

Answer

Increase the alerting threshold to reduce false positives.

Answer

Retrain the model using the latest data and redeploy.

Question 14

You run the above command to deploy a new model version to an existing endpoint. After deployment, you observe that the endpoint's previous model version is still receiving 100% of traffic. What is the most likely reason for this?

Accepted Answer

The --traffic-split flag is specified incorrectly; it should use model IDs, not '0-100'.. The traffic-split flag syntax is incorrect. The correct syntax for Vertex AI is --traffic-split== for each model. Without correct model IDs, the flag is ignored, and no traffic split is applied, so the existing version continues to receive all traffic.

Answer

The new model is still in the 'creating' state and hasn't been activated.

Answer

The model ID provided does not exist in the endpoint.

Answer

The min-replica-count is too high, preventing traffic splitting.

Question 15

A company needs to serve a large Transformer model (5 GB) with strict latency requirements (< 50 ms) and throughput of 1000 requests per second. The model is in SavedModel format. They are considering deployment options on Google Cloud. Which approach best meets these requirements?

Accepted Answer

Deploy on Vertex AI Prediction using a single high-memory VM with a GPU (e.g., n1-highmem-32 with A100).. Option A is correct because a single high-memory VM with a powerful GPU (e.g., A100) can handle the model size and throughput with low latency, avoiding network overhead. Option B is wrong because model parallelism adds complexity and may not be needed for a 5GB model on a single high-end GPU. Option C is wrong because distributed serving introduces network latency. Option D is wrong because Cloud Run currently does not support GPU instances effectively.

Answer

Deploy on Cloud Run with a GPU-enabled instance and increase concurrency.

Answer

Deploy on Vertex AI Prediction using model parallelism across multiple GPUs on a single VM.

Answer

Deploy on Vertex AI Prediction using distributed serving with TensorFlow Serving and model sharding across multiple VMs.