PMLE Practice Test 2 — 10 Questions

Question 1

A retail company deployed a demand forecasting model using TensorFlow on Vertex AI Batch Prediction. The model runs weekly on a large dataset stored in BigQuery. Over the past month, the prediction accuracy has degraded significantly. The ML engineer reviews the monitoring dashboard and sees that the feature distribution for 'product_price' has shifted from a mean of $50 to $55, and the new product category 'electronics' now represents 20% of the data, whereas it was only 5% in training. The model was never retrained after initial deployment six months ago. The engineer also notices that the Vertex Explainable AI feature importance scores have changed: 'product_price' used to be the top feature (importance 0.35) but now ranks third (importance 0.20). The company requires minimal downtime and wants to improve accuracy as quickly as possible without incurring high costs from excessive retraining. Which course of action should the ML engineer take?

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

Accepted Answer

Retrain the model using the most recent 3 months of data, including all new product categories, and deploy the updated model via a new Vertex AI endpoint.. The correct action is to retrain the model with the latest data because the feature distributions and data composition have changed significantly, indicating covariate shift and concept drift. Retraining on the most recent 3 months of data (including the new 'electronics' category) will adapt the model to the current data distribution, improving accuracy with minimal downtime by deploying via a new Vertex AI endpoint. Option A (switching to a more complex model without retraining) would not address the drift and may overfit. Option B (routing predictions to human reviewers) is costly, not scalable, and introduces latency. Option D (adjusting prediction thresholds) is insufficient because the model's internal representations are outdated due to the distribution shift, and thresholds alone cannot correct inaccurate predictions.

Answer

Increase the complexity of the model by switching from a feedforward neural network to a gradient boosted tree ensemble, and then deploy without retraining.

Answer

Route all predictions to human reviewers until the model can be re-evaluated, and then manually correct the outputs.

Answer

Adjust the prediction threshold for the 'product_price' feature to account for the price shift, and monitor for another month.

Question 2

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 B and C are correct. Optimizing the model using quantization or pruning reduces the model's size and computational requirements, directly decreasing per-request latency. Deploying the model in the same region as the clients minimizes network round-trip time, reducing overall serving latency. Option A (larger machine type) may increase throughput but does not necessarily reduce latency per request; Option D (batch prediction) is designed for high throughput, not low latency; Option E (model caching) is not a standard feature of Vertex AI endpoints for reducing latency.

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 3

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 uses custom machine types to avoid overprovisioning and reduce costs. E tunes batch size to maximize throughput per worker, reducing the number of workers needed. 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 4

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.. The correct answers are A and C. Option A is a best practice because monitoring prediction latency and error rates with Cloud Monitoring alerts helps detect performance issues and ensure availability. Option C is a best practice because using a dedicated service account with minimal permissions follows the principle of least privilege, enhancing security. Option B is not a best practice because logging all raw inputs and outputs for every request can cause privacy concerns, increase costs, and is unnecessary for most use cases. Option D is not a best practice because deploying in the same environment as training may not always be feasible; instead, models should be containerized to ensure consistency across environments. Option E is not a best practice because using the default alias 'default' for all deployments can lead to confusion and makes rollbacks more difficult; proper versioning and aliases should be used.

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 5

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. Enabling autoscaling based on CPU utilization dynamically adjusts the number of instances to handle traffic spikes, reducing latency without sacrificing accuracy. Option B increases cost without addressing scaling elasticity. Option C may reduce accuracy. Option D does not address latency spikes at the serving layer.

Answer

Use a larger machine type

Answer

Reduce model size by pruning

Answer

Implement client-side caching

Question 6

A company needs to maintain an audit trail of model changes for compliance. Multiple teams will be updating models. What is the best approach to track who created, modified, or deployed each model version?

Accepted Answer

Use Vertex AI Experiments and Metadata to track model lineage and audit logs. Vertex AI Experiments and Metadata provide a built-in way to track model lineage, including who created, modified, or deployed each version, with automatic audit logging. Option A is wrong because Cloud Storage audit logs only capture access to storage objects, not model-specific metadata or changes. Option B is wrong because Cloud Logging requires manual correlation of log entries to model versions, which is error-prone and not designed for lineage tracking. Option D is wrong because a shared spreadsheet lacks automation, access control, and auditability, making it unsuitable for compliance.

Answer

Enable Cloud Storage audit logs and require all model files to be stored in a bucket

Answer

Use Cloud Logging to collect logs from all services and search for model names

Answer

Ask team members to maintain a shared spreadsheet of changes

Question 7

A team is scaling their prototype inference model to handle high-throughput requests with low latency. They use a custom container on Vertex AI Prediction. They notice that latency spikes occur under heavy load. What is the most effective strategy?

Accepted Answer

Optimize model serving with batching and model warm-up.. Optimizing model serving with batching combines multiple requests into a single inference call, improving throughput, and model warm-up pre-loads models into memory, reducing cold-start latency spikes. Option A is wrong because auto-scaling with higher minimum replicas helps with availability but does not address per-request latency spikes; it can add replicas only after load increases. Option C is wrong because adding more CPUs may not reduce latency if the bottleneck is model inference computation or memory bandwidth; batching and warm-up are more targeted. Option D is wrong because switching to GPUs can improve throughput for compute-heavy models but may not specifically reduce latency spikes caused by request bursts; batching and warm-up directly mitigate transient spikes.

Answer

Enable auto-scaling with a higher minimum number of replicas.

Answer

Use a larger machine type with more CPUs.

Answer

Use a GPU-based machine.

Question 8

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. Vertex AI Model Registry provides model versioning, approval tracking, and lineage tracking, enabling easy collaboration across teams. Option B is wrong because ML Metadata is a lower-level tool for tracking metadata, not a user-friendly service for model versioning and approvals. Option C is wrong because Feature Store is designed for managing feature data, not models. Option D is wrong because Vizier is used for hyperparameter tuning, not model management.

Answer

Vertex AI ML Metadata

Answer

Vertex AI Feature Store

Answer

Vertex AI Vizier

Question 9

Refer to the exhibit. A team deploys a model using Cloud Run. They notice that after scaling up, the new instances take about 90 seconds to become ready and serve requests. They want to reduce this startup time. Which configuration change is most likely to help?

Accepted Answer

Change the container image to use a smaller base image. Using a smaller container image (e.g., a minimal base image) reduces image pull and initialization time, directly lowering startup latency. Option A reduces the startupProbe initialDelaySeconds, but this only shortens the time before the probe starts checking; the instance still needs to become ready. Option C reduces memory limit, which could cause out-of-memory errors if the model requires more memory, and does not improve startup time. Option D increases containerConcurrency, which affects how many requests each instance handles but does not affect startup time.

Answer

Reduce the startupProbe initialDelaySeconds to 30

Answer

Reduce the memory limit to 4Gi

Answer

Increase the containerConcurrency to 100

Question 10

You are using Vertex AI Prediction with a custom container that requires a large model file (5 GB). Deployment takes 10 minutes to start. You want to reduce cold start latency. Which action would be MOST effective?

Accepted Answer

Set minReplicas to 1 to keep at least one instance always running.. Cold start latency occurs when a new instance is started and must load the 5 GB model from disk, which can take 10 minutes. Setting minReplicas to 1 ensures that at least one instance is always running and serving, so the model is already loaded in memory, completely avoiding cold start on prediction requests. While other options (compressing the model, using local SSD, or switching to batch prediction) may reduce load time or avoid online serving, they do not eliminate the cold start penalty as effectively as maintaining a warm replica.

Answer

Compress the model file and decompress on startup.

Answer

Use a machine type with local SSD to speed up model loading.

Answer

Switch to batch prediction to avoid online cold start.