PMLE Practice Test 28 — 15 Questions

Question 1

After deploying a new version of a model to a Vertex AI Endpoint, the team notices that predictions are still returning results from the old version. The deployment command used a traffic split of 100% to the new version. What is the most likely cause?

Accepted Answer

The traffic split was not properly updated; the endpoint is still routing 100% to the old version.. Option A is correct because the traffic split update may not have taken effect if the command failed silently, or the new version is not healthy, causing the endpoint to route traffic to the old version. Option B is wrong because caching is not a typical issue for Vertex AI Endpoint. Option C is wrong because the deployment succeeded but traffic split might need explicit update. Option D is wrong because a stale model artifact would affect the new version only.

Answer

The model artifact uploaded was identical to the old version.

Answer

The new model version failed health checks and was automatically rolled back.

Answer

The prediction client is caching the old model response.

Question 2

You deploy a PyTorch model to Vertex AI Online Prediction. After deployment, you observe that inference latency is approximately 300ms per request, but the desired SLA is under 100ms. The model uses a custom container with CPU only. Which action is most likely to reduce latency to the target?

Accepted Answer

Deploy the model on a machine with a GPU accelerator.. Enabling GPU acceleration can significantly speed up inference for deep learning models. Adding more CPU instances may help with throughput but not per-request latency. Switching to batch prediction changes the use case, and using a smaller instance type might reduce latency if the model is small, but GPU is more impactful.

Answer

Switch from online prediction to batch prediction.

Answer

Increase the min_replica_count to ensure more instances are always available.

Answer

Use a smaller machine type with less CPU to reduce overhead.

Question 3

You have a model that requires GPU for efficient inference. You deploy it on Vertex AI with a single NVIDIA T4 GPU accelerator and notice that the GPU utilization hovers around 30%. The endpoint has 10 replicas. What is the best way to improve cost efficiency while maintaining throughput?

Accepted Answer

Reduce the number of replicas to increase GPU utilization per instance.. If GPU utilization is low, you can reduce the number of replicas or increase the batch size per request to fully utilize the GPU. Reducing replicas directly saves cost. Increasing batch size may also help but requires code changes.

Answer

Use a larger GPU like V100 to process requests faster.

Answer

Enable autoscaling to increase the number of replicas.

Answer

Switch to a CPU-only instance; the model can run on CPU.

Question 4

A company wants to deploy a model for real-time inference with high availability across multiple Google Cloud regions. The model is small and stateless. Which two steps should they take? (Choose two.)

Accepted Answer

Deploy the model to Vertex AI Prediction endpoints in multiple regions and use a global external HTTP(S) load balancer to route traffic to the nearest region.. Options B and C are correct. B deploys the model to Vertex AI Prediction endpoints in multiple regions behind a global load balancer, providing regional failover. C uses Cloud Run with multi-region deployment and a global load balancer, which also offers multi-region HA. Option A is insufficient as a single region does not survive a regional outage. Option D is wrong because Cloud Functions is region-specific and not designed for latency-sensitive inference across regions. Option E is wrong because a single region does not provide cross-region HA.

Answer

Use Cloud Functions with a global HTTP(S) load balancer.

Answer

Use a single Vertex AI Prediction endpoint with multiple replicas across zones in the same region.

Answer

Deploy the model to a Vertex AI Prediction endpoint in a single region and use a global external HTTP(S) load balancer.

Question 5

You manage a multi-tenant serving system on Vertex AI Prediction where multiple models are deployed in a single endpoint using model versioning. One particular model version (v2) is consuming excessive resources, causing latency spikes for other versions. You need to isolate this model to prevent interference. The models are all in TensorFlow SavedModel format. What is the best approach?

Accepted Answer

Use Vertex AI Model Registry to deploy v2 to a dedicated endpoint and update the model alias.. Option B is correct because creating a separate endpoint for v2 provides full resource isolation. Option A is similar but less direct (load balancer still distributes to same endpoint). Option C is not possible in Vertex AI Prediction. Option D is complex and error-prone.

Answer

Shard the models across multiple replicas using a custom routing logic in the container.

Answer

Set resource limits on the container using Kubernetes resource requests/limits, but Vertex AI Prediction does not support that.

Answer

Create a separate endpoint for v2 and redirect traffic using a load balancer.

Question 6

A financial services company has deployed a credit risk ML model on Vertex AI. They want to monitor the model for fairness across demographic groups to ensure no biased outcomes. Which TWO actions should they take as best practices? (Choose TWO.)

Accepted Answer

Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups.. Option B is correct because Vertex Explainable AI provides feature attribution scores that can be compared across demographic groups to detect if the model relies on sensitive attributes or proxies. This enables fairness auditing by revealing whether the model's decision logic differs systematically for protected groups, which is a best practice for monitoring bias.

Answer

Eliminate all features that are correlated with protected attributes from the model input to ensure fairness.

Answer

Periodically compare the model's performance metrics (e.g., AUC) on the overall population versus the holdout test set.

Answer

Store all model predictions in BigQuery but do not capture ground truth labels to avoid privacy issues.

Question 7

Refer to the exhibit. A machine learning engineer deployed a model on Vertex AI using this configuration. When testing the endpoint, the engineer receives a 400 error with the message: 'Invalid argument: Explanation metadata missing required field: `outputs`.' What is the most likely cause?

Accepted Answer

The explanation metadata outputs field is missing the required 'displayName' attribute.. The error message indicates that the explanation metadata provided in the Vertex AI endpoint configuration is missing the required `outputs` field. In Vertex AI's Explainable AI, the `outputs` field must contain at least one entry with a `displayName` attribute to define which output tensor to explain. Without this, the API rejects the request with a 400 error.

Answer

The explanation metadata needs a 'baseline' configuration for the input.

Answer

The explanation metadata inputs field should be wrapped inside a 'visualization' block.

Answer

The explainability method chosen is not supported for the model type.

Question 8

An ML engineer is deploying a large BERT-based natural language processing model for real-time inference on Vertex AI Prediction. The model has a large memory footprint (2GB) and experiences unpredictable traffic spikes up to 10x the baseline. The engineer needs to minimize latency and cost while handling spiky traffic. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Use a custom prediction routine with model quantization to reduce model size.. Option C is correct because model quantization reduces the memory footprint of a BERT model (e.g., from 2GB to ~500MB with INT8 quantization), which directly lowers inference latency and cost by enabling faster loading and more efficient use of hardware. This is critical for real-time inference with unpredictable traffic spikes, as smaller models scale more easily and reduce the need for excessive replicas.

Answer

Configure the endpoint to use manual scaling with a fixed number of replicas equal to peak traffic.

Answer

Enable automatic scaling with a maximum of 3 replicas to limit cost.

Answer

Set up model monitoring to detect prediction drift and retrain regularly.

Question 9

Refer to the exhibit. A Machine Learning Engineer attempts to deploy a model to a Vertex AI Endpoint for online predictions but receives an error. What is the most likely cause of this error?

Accepted Answer

The min replica count is set to 0, which is not allowed for online prediction.. Vertex AI online prediction endpoints require at least one replica to serve traffic. Setting `min_replica_count` to 0 is only valid for batch prediction, not for online prediction, because the endpoint must always have a running instance to handle incoming requests. The error occurs because the deployment request violates this constraint, causing the API to reject the configuration.

Answer

The model is not compatible with the selected machine type.

Answer

The machine type does not support GPU acceleration.

Answer

The endpoint is not in the same region as the model.

Question 10

A data science team has trained a custom model using Vertex AI and wants to deploy it for online predictions with low latency. Which TWO actions should they take to optimize performance?

Accepted Answer

Optimize the model by quantizing to FP16.. Option C is correct because quantizing the model to FP16 reduces its memory footprint and computational requirements, directly lowering inference latency on compatible hardware (e.g., NVIDIA GPUs with Tensor Cores). This optimization is especially effective for online predictions where response time is critical, as it accelerates matrix operations without significantly sacrificing model accuracy.

Answer

Use Vertex AI Endpoints with traffic splitting for canary deployments.

Answer

Enable autoscaling with a large min replicas count to handle bursts.

Answer

Use a machine type with GPU for inference.

Question 11

A pharmaceutical company uses Vertex AI Pipelines with custom training containers. Recently, the pipeline has been failing with 'Container failed with exit code 137' (out of memory). The container runs with default memory limit. The team needs to fix this without changing the code. The project quota for CPU and memory is sufficient. What should the team do?

Accepted Answer

Set the 'machineType' field for the training task to a higher memory machine.. Option B is correct because the container is running out of memory (exit code 137) with the default memory limit. In Vertex AI Pipelines, when using custom training containers, the default memory allocation is typically 4 GiB. By setting the 'machineType' field to a higher memory machine (e.g., n1-highmem-8), the container automatically receives more memory without requiring code changes. This directly resolves the OOM issue while respecting the constraint of not modifying the code.

Answer

Add a resource hint to the container spec for more memory.

Answer

Increase the model parallelism by using multi-worker training.

Answer

Use a smaller dataset for training.

Question 12

You are a Machine Learning Engineer at a financial services company. You have trained a large language model (LLM) using a custom container on Vertex AI Training. The model is used for sentiment analysis on financial news articles. You have deployed the model to a Vertex AI Endpoint for online prediction. However, during peak trading hours, users report high latency ( > 5 seconds) and occasional timeout errors. The model is deployed on n1-highmem-8 machines with 1 replica. You monitor the endpoint and see that CPU utilization is high ( > 90%) and memory is near capacity. The queries are relatively small text inputs. Which course of action should you take to reduce latency?

Accepted Answer

Use Vertex AI Prediction with GPU accelerators like NVIDIA Tesla T4.. Option B is correct because the high CPU utilization and memory pressure indicate that the CPU is the bottleneck for inference, not the model size or input volume. Switching to GPU accelerators like NVIDIA Tesla T4 offloads the computationally intensive matrix operations of the LLM to the GPU, drastically reducing per-query latency and freeing CPU resources for preprocessing and I/O. This directly addresses the root cause of >5-second latency during peak hours.

Answer

Deploy the model to multiple endpoints and use round-robin load balancing.

Answer

Increase the machine type to n1-highmem-16 and keep 1 replica.

Answer

Reduce the batch size for predictions to lower memory usage.

Question 13

An ML engineer notices that predictions are taking longer than expected under moderate traffic. Reviewing the endpoint configuration, what is the most likely cause of the high latency?

Accepted Answer

The accelerator count is 0, meaning no GPU is used.. When the accelerator count is set to 0, the endpoint runs inference on the CPU only, which is significantly slower than GPU-accelerated inference for deep learning models. This is the most direct cause of high latency under moderate traffic, as the model's compute demands exceed CPU throughput.

Answer

Container logging is disabled, slowing down request processing.

Answer

The machine type n1-standard-4 is underpowered for the model's compute needs.

Answer

Automatic scaling is set with a maxReplicaCount of 10, which creates overhead.

Question 14

A data science team uses Vertex AI Workbench and wants to share notebooks with version history. Which service should they use?

Accepted Answer

Cloud Source Repositories. Cloud Source Repositories (CSR) is the correct choice because it provides Git-based version control for notebooks, enabling teams to track changes, collaborate, and maintain a full version history. Vertex AI Workbench integrates natively with CSR, allowing users to clone, commit, and push notebook files directly from the JupyterLab interface, which is essential for collaborative development with revision tracking.

Answer

Artifact Registry

Answer

Cloud Storage

Answer

Data Catalog

Question 15

A team uses Vertex AI Pipelines. They need to ensure that only certain team members can deploy models to production. What is the best approach?

Accepted Answer

Use IAM roles with custom permissions on the Vertex AI Model Registry. Option C is correct because Vertex AI Model Registry supports IAM roles with custom permissions, allowing fine-grained access control over who can promote or deploy models to production. By assigning specific roles (e.g., `roles/aiplatform.modelDeployer`) to only authorized team members, you can restrict deployment actions while still permitting others to view or register models. This approach directly addresses the need to control production deployments without affecting other pipeline stages.

Answer

Use Vertex AI Experiments to track models

Answer

Store model artifacts in a bucket with bucket-level permissions

Answer

Create separate projects for dev and prod