PMLE Practice Test 5 — 10 Questions

Question 1

Your team deploys a multi-model endpoint on Vertex AI with two models: Model A (small, low latency) and Model B (large, high latency). You configure traffic splitting so that 90% goes to Model A and 10% to Model B. However, you notice that the latency for Model A increases when Model B receives traffic. What is the most likely cause?

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

Accepted Answer

The models are collocated on the same instances, leading to resource contention.. In a multi-model endpoint, all models share the underlying infrastructure. When Model B handles requests, it consumes resources (CPU/memory), causing contention that degrades Model A's latency. Collocation of models on the same instance is the issue.

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Model A is being overloaded because autoscaling is based on aggregate traffic.

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The traffic split is misconfigured, causing requests to be routed incorrectly.

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Model B's logging is generating too much output, slowing down the predictor.

Question 2

A data scientist deployed a model to Vertex AI Prediction. When making a prediction request as shown in the exhibit, they receive a 400 error. What is the most likely cause?

Accepted Answer

The model was trained on 2 features, but the request provides 3 features.. The 400 error indicates a malformed request, typically due to a mismatch between the input features the model expects and what is provided. Since the model was trained on 2 features but the request includes 3 features, Vertex AI rejects the prediction as invalid input shape mismatch. This is the most common cause of 400 errors in Vertex AI Prediction when the instance structure does not match the model's signature.

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The request JSON is malformed due to a missing comma between instances.

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The endpoint path is incorrect; it should include the model version.

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The request is sending 3 separate instances but the model expects only 1.

Question 3

A data scientist uses Vertex AI Pipelines to orchestrate an ML workflow. They want to reuse a component from Google's curated repository. What is the recommended way to incorporate it?

Accepted Answer

Use prebuilt components from the Google Cloud Pipeline Components repository. Google provides a curated set of prebuilt components in the Google Cloud Pipeline Components repository, which are designed to be directly imported and used within Vertex AI Pipelines. These components encapsulate common ML tasks (e.g., model training, deployment) and are maintained by Google, ensuring compatibility and reducing custom code. Using them is the recommended approach to avoid reinventing the wheel and to leverage Google's best practices.

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Import the component from Google Cloud Build

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Use the 'aiplatform' Python SDK to define the component

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Copy the component code into the pipeline definition

Question 4

A media company wants to transcribe audio files from customer support calls into text for analysis. The audio is in English with clear speech and no background noise. They want a quick solution with no ML model training. Which Google Cloud service should they use?

Accepted Answer

Speech-to-Text API with the latest_long model. Speech-to-Text is a pre-built API for transcribing audio to text. It is ready to use without training. AutoML NLP is for text classification, not transcription. Vertex AI Workbench and Translation API are not relevant.

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Translation API to translate the audio

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AutoML NLP to train a transcription model

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Vertex AI Workbench to train a custom speech recognition model

Question 5

Which TWO actions are recommended to detect and mitigate data drift in a production ML system on Vertex AI?

Accepted Answer

Automatically retrain the model when drift exceeds thresholds. Vertex AI's automated retraining pipeline can be triggered when data drift exceeds a predefined threshold, ensuring the model adapts to distribution changes without manual intervention. Option D is correct because Vertex AI Model Monitoring continuously tracks feature distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) and sends alerts when drift is detected, enabling proactive mitigation.

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Deploy multiple models and use an ensemble to average predictions

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Manually review model predictions daily

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Monitor prediction errors and flag when confidence is low

Question 6

You are responsible for maintaining an ML pipeline that runs daily on Vertex AI Pipelines. The pipeline preprocesses data, trains a model, and deploys it to an endpoint. Recently, the pipeline has been failing at the deployment step because the endpoint already exists and the deploy step tries to create a new endpoint instead of updating the existing one. The pipeline code is written using the Kubeflow Pipelines SDK. You need to modify the pipeline to resolve this issue with minimal changes. What should you do?

Accepted Answer

In the deployment component, add a check to verify if the endpoint exists, and if so, call the update endpoint method instead of create.. The issue is that the deployment component always tries to create a new endpoint, which fails if the endpoint already exists. To resolve this with minimal changes, the deployment component should first check if the endpoint exists. If it does, it should update the existing endpoint instead of creating a new one. This approach aligns with best practices for CI/CD pipelines. Option A is incorrect because introducing a Cloud Function bypasses the pipeline orchestration and adds unnecessary complexity. Option C is incorrect because retrying will not fix the fundamental issue of attempting to create an existing endpoint. Option D is incorrect because manually deleting the endpoint before each run defeats automation and is not a robust solution.

Answer

Change the pipeline to use a Cloud Function that triggers the deployment independently, bypassing Vertex AI Pipelines.

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Set the deploy component's retry policy to infinite so it eventually succeeds.

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Manually delete the existing endpoint before each pipeline run.

Question 7

You are a machine learning engineer at a financial technology company. You have deployed a complex ensemble model consisting of three sub-models (XGBoost, TensorFlow, and PyTorch) for real-time fraud detection. The model is served on Vertex AI online prediction with a custom container that orchestrates the three models sequentially. The endpoint currently uses n1-highmem-8 machines with no accelerators. You are experiencing high latency (avg 500ms) during peak trading hours (9:30 AM - 4:00 PM EST), exceeding the 200ms SLA. The container is CPU-bound, and memory usage is around 60%. The model weights total 500 MB. You have already tried increasing the batch size per request from 1 to 4, which reduced latency slightly but not enough. The traffic pattern is very spiky, with sudden bursts of up to 1000 requests per second. Your goal is to meet the latency SLA without significantly increasing cost. Which action should you take?

Accepted Answer

Add a NVIDIA T4 GPU accelerator to the existing machine type.. Adding a GPU accelerator (e.g., NVIDIA T4) to the instances can significantly speed up the TensorFlow and PyTorch components, which are deep learning models. The XGBoost part runs on CPU but the overall latency bottleneck is likely the deep learning models. GPU will accelerate inference of those models, reducing total latency. Increasing CPUs will help only marginally as the main bottleneck is compute. Reducing min replicas may increase cold start and queue. Switching to batch prediction changes the model from real-time to batch, which does not meet the latency requirement.

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Reduce the min_replica_count to 0 to allow scaling down aggressively and add more replicas during spikes.

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Increase the machine type to n1-highmem-16 with more vCPUs.

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Switch the model to Vertex AI batch prediction and run predictions every hour.

Question 8

A team prototypes a recommendation model using a Jupyter notebook on Vertex AI Workbench. They want to productionize the model with CI/CD. Which approach should they use to package the model for deployment?

Accepted Answer

Upload the model to Vertex AI Model Registry and use it for deployment. Vertex AI Model Registry is the central repository for managing ML models, enabling versioning, evaluation, and deployment to endpoints. This approach integrates with CI/CD pipelines via the Vertex AI SDK or Cloud Build, allowing automated model promotion and deployment without manual packaging. Option D directly leverages Vertex AI's native deployment workflow, which is the recommended path for productionizing models from Workbench.

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Use Cloud Build to deploy the notebook directly as a prediction endpoint

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Store the model in Cloud Source Repositories and deploy from there

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Containerize the model and push to Artifact Registry, then deploy via Cloud Run

Question 9

A company implements an ML pipeline using Vertex AI Pipelines. The pipeline trains a model using custom training jobs and then deploys it to an endpoint. The team notices that the endpoint occasionally serves an older model version for a few minutes after a new pipeline run completes. What is the most likely cause?

Accepted Answer

The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout.. D is correct because Vertex AI endpoints can be configured with a canary (gradual) traffic rollout strategy. When a new model is deployed, traffic is shifted incrementally from the old model to the new one over a specified duration. During this rollout window, the old model continues to serve a fraction of requests, which explains why users occasionally see the older model version for a few minutes after the pipeline completes.

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The new model artifact is temporarily unavailable, so the endpoint falls back to the previous version.

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The prediction cache is returning cached results from the old model.

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The pipeline failed to update the endpoint with the new model ID.

Question 10

A company runs a Vertex AI pipeline that uses a container component to preprocess data. The component downloads a large file from a public URL and saves the output to Cloud Storage. The pipeline fails intermittently with a 'timeout' error. Which THREE steps should the team take to improve reliability? (Choose three.)

Accepted Answer

Make the component idempotent by checking for existing output before processing.. Making the component idempotent by checking for existing output before processing prevents redundant work and avoids timeout failures when the file has already been downloaded. In Vertex AI pipelines, idempotent components can safely skip processing if the output already exists in Cloud Storage, reducing the risk of hitting timeout limits on subsequent pipeline runs.

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Reduce the size of the file being downloaded.

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Increase the machine type for the component.