PMLE Practice Exam 3 — 20 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.

Answer

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.

Answer

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.

Question 11

You are designing a batch prediction pipeline using Vertex AI. The input data is 100 TB of images stored in Cloud Storage. The model is a custom TensorFlow model that expects TFRecord format. The pipeline must be cost-effective and run within a time window of 2 hours. Which THREE steps should you include?

Accepted Answer

Create a Vertex AI batch prediction job with input from GCS (TFRecord files).. Vertex AI batch prediction jobs natively accept TFRecord files stored in Cloud Storage as input, which aligns with the requirement for a custom TensorFlow model. This approach is cost-effective and can complete within 2 hours by leveraging Vertex AI's managed infrastructure, avoiding the need to spin up and manage compute resources manually.

Answer

Store batch prediction results in BigQuery.

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Use Cloud Functions to convert images to TFRecord.

Question 12

A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)

Accepted Answer

Deploy the model on a GPU-enabled machine. Deploying the model on a GPU-enabled machine significantly accelerates matrix operations and parallel computations inherent in deep learning inference, directly reducing per-request latency. Option C is correct because model quantization reduces the precision of model weights (e.g., from FP32 to INT8), which decreases memory footprint and speeds up computation, especially on compatible hardware like TPUs or GPUs.

Answer

Increase the maximum number of replicas

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Use a smaller machine type with less memory

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Enable autoscaling with a lower target CPU utilization

Question 13

You have a Vertex AI pipeline that trains a model and outputs a Model artifact. You want to register this model in the Vertex AI Model Registry. Which pre-built Google Cloud Pipeline Components component should you use?

Accepted Answer

VertexModelUploadOp. The correct component is VertexModelUploadOp because it is specifically designed to upload a trained model artifact to the Vertex AI Model Registry, creating a new model version or a new model if one does not exist. This component takes the model artifact from a pipeline step and registers it, making it available for deployment or version management.

Answer

VertexEndpointDeployOp

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CreateModelVersionsOp

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ModelRegisterOp

Question 14

You have an online prediction model that is showing increasing prediction latency. You have already verified that the request rate and input data size are unchanged. Which of the following should you investigate next?

Accepted Answer

Check if the model was recently updated to a larger version. If request rate and input data size are unchanged, increased prediction latency often points to a change in the model itself. A larger model (e.g., deeper neural network, more parameters) requires more computation per inference, directly increasing latency. This is a common root cause when monitoring ML pipelines, as model version updates can silently alter performance characteristics.

Answer

Check the monitoring dashboard configuration

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Check if the feature engineering logic was changed

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Check the geographic location of the endpoint

Question 15

An organization wants to implement continuous delivery for their ML model. After a new model is trained and evaluated, they want to automatically deploy it to a staging endpoint, run validation tests, and if passed, promote to production. Which two components should they include in their delivery pipeline? (Choose two.)

Accepted Answer

Conditional component that checks evaluation metrics and promotes if successful. A conditional component that checks evaluation metrics (e.g., accuracy, precision, recall) against a predefined threshold is essential for automated promotion. This component acts as a gate, ensuring only models that meet quality criteria are promoted to production, which is a core requirement for continuous delivery in ML pipelines.

Answer

Dataflow job to preprocess data

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Importer component to bring in the model

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A/B testing component to split traffic

Question 16

A data scientist wants to deploy a model trained with PyTorch to a Vertex AI endpoint for online predictions. What is the recommended approach?

Accepted Answer

Package the model in a custom container with a web server (e.g., FastAPI) and deploy to Vertex AI.. Vertex AI requires a custom container for PyTorch models, as it does not provide a pre-built PyTorch serving container. The recommended approach is to package the trained PyTorch model with a web server like FastAPI (or Flask) that loads the model and exposes an HTTP endpoint for predictions. This container is then deployed to Vertex AI for online predictions, giving full control over the inference environment and dependencies.

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Use Vertex AI's pre-built PyTorch container and upload the state dictionary.

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Export the PyTorch model to a SavedModel format and deploy using Vertex AI's pre-built TensorFlow container.

Answer

Convert the model to TensorFlow.js and deploy to Cloud Functions.

Question 17

A company wants to automatically retrain their model every night at 2 AM using Vertex AI Pipelines. Which approach should they use to trigger the pipeline on a schedule?

Accepted Answer

Use Cloud Scheduler to call the Vertex AI pipeline creation API. Cloud Scheduler is the correct approach because it can directly invoke the Vertex AI Pipeline creation API via an HTTP trigger at a specified cron schedule (e.g., 2 AM daily). This integrates natively with Vertex AI's pipeline orchestration, allowing the scheduler to submit a pipeline run without additional infrastructure. The other options either lack native Vertex AI pipeline support or introduce unnecessary complexity.

Answer

Deploy the pipeline as a Cloud Run job with a cron trigger

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Use Vertex AI Experiments to schedule runs

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Configure a cron job inside the pipeline definition

Question 18

A company uses Vertex AI Prediction with a custom container for a TensorFlow model. They notice that after deploying a new model version, requests still go to the old version. What is the most likely cause?

Accepted Answer

Traffic is not split to the new model version. In Vertex AI Prediction, when you deploy a new model version to an existing endpoint, you must explicitly allocate traffic to it. By default, the new version receives 0% traffic, so all requests continue to be served by the old version. The correct fix is to update the endpoint's traffic split, for example via the console or the `gcloud ai endpoints update` command with the `--traffic-split` flag.

Answer

The custom container is not compatible with Vertex AI

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The model is cached and needs cache invalidation

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The new model version was not deployed to the same endpoint

Question 19

A data science team needs to serve multiple versions of the same ML model on Vertex AI Endpoints for A/B testing. They want to gradually shift traffic from the current 'champion' model to a new 'challenger' model. Which feature should they use?

Accepted Answer

Deploy both models to the same endpoint and use traffic splitting.. Vertex AI Endpoints natively support traffic splitting, allowing you to deploy multiple model versions (e.g., champion and challenger) to the same endpoint and assign a percentage of traffic to each. This enables gradual A/B testing without additional infrastructure, as the endpoint automatically routes requests based on the configured split. Option C is correct because it leverages this built-in feature, which is designed specifically for this use case.

Answer

Deploy the challenger to a separate endpoint and use a proxy to split traffic.

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Use Cloud Load Balancing with weighted backend services.

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Use Vertex AI Experiments to manage model versions.

Question 20

A team is using Cloud Composer to orchestrate ML workflows. They want to allow multiple data scientists to contribute DAGs without interfering with each other. What is the recommended approach?

Accepted Answer

Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer. Cloud Composer (based on Apache Airflow) recommends managing DAGs via source control and CI/CD pipelines to ensure version control, code review, and consistent deployment. This prevents conflicts when multiple data scientists contribute, as each change is tracked and tested before being synced to the DAGs folder in Cloud Storage, avoiding overwrites or broken workflows.

Answer

Give each data scientist write access to the DAGs folder in Cloud Storage

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Use a complex naming convention for DAG files to avoid overwriting

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Create a separate Cloud Composer environment for each data scientist