PMLE Practice Test 35 — 15 Questions

Question 1

A team is deploying a scikit-learn model to Vertex AI for online prediction. The model requires a custom preprocessing step that scales numerical features using statistics computed from the training set. The preprocessing must be identical between training and serving. The team wants to minimize latency and ensure consistency. What should they do?

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

Accepted Answer

Wrap the preprocessing and model into a single scikit-learn Pipeline object, and deploy that pipeline as a custom model on Vertex AI.. Wrapping preprocessing and the model into a single scikit-learn Pipeline ensures that the exact same transformations are applied during both training and serving. This eliminates training-serving skew and simplifies deployment. Vertex AI supports deploying custom models with custom prediction routines, but using a native scikit-learn Pipeline is more straightforward and less error-prone, as it encapsulates all steps and can be serialized as one artifact.

Answer

Use a Vertex AI pipeline to preprocess the data before training, and then deploy the model without preprocessing, assuming the input data at serving time will already be scaled.

Answer

Save the scikit-learn model and the scaler as separate artifacts, and in the prediction script, load both and apply the scaler before calling model.predict.

Answer

Use Vertex AI Feature Store to store the scaled features and serve them online, bypassing the need for preprocessing in the model.

Question 2

A fraud detection model is deployed to a Vertex AI Endpoint and configured with Vertex AI Model Monitoring for feature drift. The team wants the drift monitor to compare live production traffic against the exact statistics captured from the training dataset, so that alerts reflect deviation from the model's original data distribution rather than from recent traffic. Which configuration should they use?

Accepted Answer

Set the monitoring training dataset to the original training data and enable drift detection.. Feature drift detection in Vertex AI Model Monitoring requires a baseline distribution to compare against. Using the original training data as the baseline anchors the comparison to the model's training distribution, which is exactly what the team wants. Other options either change the comparison window, switch to prediction drift, or alter scheduling without defining the reference, so they do not meet the stated requirement.

Answer

Enable prediction drift instead of feature drift and let Vertex AI infer the baseline automatically.

Answer

Configure the monitor to use a rolling 24-hour window of live requests as the baseline distribution.

Answer

Attach the training dataset as a Vertex AI managed dataset and set the monitoring frequency to daily.

Question 3

Your organization uses Vertex AI Feature Store to serve features for a real-time fraud detection model. Multiple teams contribute features, and you need to ensure that feature values are consistent between training and serving. Which practice should you implement to prevent training-serving skew?

Accepted Answer

Use a single featurestore for both training and serving, and ingest features via the same pipeline.. Training-serving skew arises when feature values differ between training and serving due to disparate data processing. Using a single featurestore and a unified ingestion pipeline ensures that features are computed and stored consistently, so the model sees identical feature distributions in both phases. This is a fundamental best practice in ML engineering.

Answer

Implement a separate feature engineering pipeline for training and another for serving to optimize each for its specific needs.

Answer

Export features from the featurestore to BigQuery for training, and use the featurestore online serving for predictions.

Answer

Use Vertex AI Feature Store's built-in monitoring to detect skew, and retrain the model when skew is detected.

Question 4

An ML team wants to monitor feature drift in their production model. Which Vertex AI Feature Store capability should they use?

Accepted Answer

Feature monitoring (drift detection). Feature monitoring (drift detection) is the Vertex AI Feature Store capability that computes drift and skew metrics for feature values against a baseline and emits them for alerting. It is purpose-built to detect when production feature distributions diverge from training or reference distributions. This directly answers the requirement to monitor feature drift.

Answer

Feature views

Answer

Online store

Answer

Point-in-time retrieval

Question 5

A team is collaborating on a Vertex AI model using Vertex AI Model Registry. They need to ensure that model versions are properly managed and that deployments are reproducible. Which TWO practices should they follow? (Choose two.)

Accepted Answer

Assign each model version a unique alias that maps to a specific model artifact and container image.. Using aliases to map model versions to specific artifacts and container images, and recording pipeline parameters and data snapshot URIs in metadata, together ensure that deployments are reproducible and that team members can collaborate effectively. Aliases provide stable references, while metadata captures the training context for audits and retraining.

Answer

Use a single default version for all deployments to simplify endpoint configuration.

Answer

Rely on the model's auto-generated resource name to identify versions, as it includes a timestamp.

Answer

Grant all team members the Vertex AI User role on the project to allow unrestricted model uploads.

Question 6

You are deploying a prototype model to Vertex AI for online prediction. The model was trained with a custom preprocessing step that must run on raw JSON input before inference. You need the endpoint to return predictions with minimal latency and to support rolling updates of new model versions without downtime. (Choose two.)

Accepted Answer

Deploy the model to an endpoint with a traffic split between the current version and a new version, gradually shifting traffic to the new version.. Bundling preprocessing and model inference into one custom container that follows the Vertex AI HTTP prediction contract keeps the request path short and self-contained, which minimizes latency. Deploying model versions to the same endpoint with a traffic split enables canary or rolling updates, so new versions can be validated with a small share of live traffic before full promotion, achieving zero-downtime releases.

Answer

Deploy the preprocessing step as a separate Vertex AI endpoint and call it from the client before sending data to the model endpoint.

Answer

Use a Vertex AI batch prediction job for the initial rollout and switch to online prediction after the model is validated.

Answer

Store the preprocessing logic in a Cloud Function and invoke it from the model's prediction container at request time.

Question 7

A data science team has deployed a model on Vertex AI and wants to automatically detect when the distribution of a specific feature shifts significantly from the training data. Which service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct service because it is specifically designed to detect feature distribution drift (skew) between training and serving data for deployed models. It continuously monitors the input features and alerts when statistical metrics like the Jensen-Shannon divergence or the L-infinity distance exceed a configured threshold, enabling proactive model retraining.

Answer

Cloud Data Loss Prevention

Answer

Vertex AI Explainable AI

Answer

Cloud Composer

Question 8

A team is deploying a large PyTorch model for online inference. They want to use NVIDIA Triton Inference Server to optimize serving performance. How can they integrate Triton with Vertex AI?

Accepted Answer

Package the model with Triton in a custom container and deploy it to Vertex AI. Vertex AI supports custom containers for prediction, so the standard integration pattern is to build a container that bundles the model with NVIDIA Triton Inference Server and deploy it as a Vertex AI Model with a custom prediction container. This lets Triton handle dynamic batching, model ensembles, and multi-framework serving while Vertex AI manages endpoints, autoscaling, and monitoring.

Answer

Vertex AI automatically uses Triton for all PyTorch models

Answer

Deploy the model to GKE and use Vertex AI as a frontend

Answer

Use a prebuilt Vertex AI PyTorch container that includes Triton

Question 9

You are collaborating with a team of data scientists on a Vertex AI Workbench notebook that preprocesses data for a machine learning model. You need to ensure that all team members can work on the notebook simultaneously without overwriting each other's changes, and that the notebook's execution environment remains consistent across the team. (Choose two.)

Accepted Answer

Store the notebook in a Cloud Source Repositories repository and use Git for version control.. Git-based version control via Cloud Source Repositories enables concurrent work with conflict resolution, while a shared custom container image ensures a consistent environment. Together, they allow team members to collaborate effectively without overwriting changes and with identical dependencies. Other options either lack robust version control or misstate Vertex AI Workbench capabilities.

Answer

Use Vertex AI Workbench's managed notebooks, which automatically sync all user changes to a central repository without requiring Git.

Answer

Enable the notebook's built-in real-time collaboration feature, which allows multiple users to edit the same notebook simultaneously.

Answer

Schedule regular exports of the notebook to a shared Cloud Storage bucket, and have team members import the latest version before making changes.

Question 10

Your team uses Vertex AI Pipelines to automate the training and deployment of a recommendation model. The pipeline includes a step that evaluates the model and only deploys it if the evaluation metric exceeds a threshold. You need to ensure that the pipeline's artifacts, including the evaluation metrics and the deployed model, are tracked and can be traced back to the pipeline run for auditing. What should you do?

Accepted Answer

Use Vertex AI ML Metadata to log the evaluation metrics and model artifacts, and associate them with the pipeline run.. Vertex AI ML Metadata provides automatic lineage tracking for pipeline artifacts, including metrics and models. It records relationships between executions and artifacts, enabling auditing and reproducibility. Other methods lack the structured, integrated metadata store that ML Metadata offers, making them less reliable for tracing and compliance.

Answer

Configure the pipeline to send an email with the evaluation metrics and model details to a distribution list for record-keeping.

Answer

Enable Cloud Logging for the pipeline and rely on the logs to capture the evaluation metrics and model deployment events.

Answer

Store the evaluation metrics in a BigQuery table and the model in Cloud Storage, and record the pipeline run ID in both locations.

Question 11

A media company wants to build a real-time recommendation system for articles. They have a large user base (10M+) and frequent updates to user interactions. They need to handle cold-start users and new articles. Which architecture on Vertex AI is most suitable?

Accepted Answer

Implement a two-tower model (user and item towers) with embeddings and nearest neighbor search. The two-tower model (user and item towers) with embeddings and nearest neighbor search is the most suitable because it handles cold-start users and new articles by learning separate embeddings for users and items, enabling efficient retrieval via approximate nearest neighbor (ANN) search. This architecture supports real-time updates and scales to 10M+ users by decoupling user and item representations, allowing incremental training on new interactions without full retraining.

Answer

Deploy a Deep Learning Recommendation Model (DLRM) for prediction

Answer

Use a contextual bandit algorithm for exploration only

Answer

Use matrix factorization with collaborative filtering

Question 12

You have trained a scikit-learn model and saved it as a joblib file in Cloud Storage. You need to deploy this model to Vertex AI for online predictions with minimal effort. What should you do?

Accepted Answer

Use the pre-built scikit-learn container provided by Vertex AI and specify the model artifact path in Cloud Storage.. Vertex AI offers pre-built containers for scikit-learn that can load joblib files directly. By using the pre-built container and specifying the model artifact path, you can deploy the model with minimal effort. Writing a custom container or converting the model format adds unnecessary work, and Model Registry does not support containerless deployment.

Answer

Upload the joblib file to Vertex AI Model Registry and deploy it directly without specifying a container.

Answer

Convert the scikit-learn model to TensorFlow SavedModel format and use the pre-built TensorFlow container.

Answer

Write a custom container that loads the joblib file and serves predictions using Flask, then deploy it to Vertex AI.

Question 13

You are deploying a model to a Vertex AI Endpoint that will serve predictions to a global user base. You want to minimize latency for users in different regions while ensuring high availability. What should you do?

Accepted Answer

Deploy the model to multiple endpoints in different regions and use a global load balancer with latency-based routing.. To minimize latency for a global user base, the model should be deployed in multiple regions so that users can be served from a nearby location. A global load balancer with latency-based routing ensures that each user is directed to the closest healthy endpoint, reducing latency and providing failover for high availability. Single-region deployments, even with larger machines or multiple endpoints in one region, cannot overcome geographic latency.

Answer

Deploy the model to multiple endpoints in the same region and use a global load balancer.

Answer

Deploy the model to a single endpoint in one region and use a global load balancer to route traffic.

Answer

Deploy the model to a single endpoint with a larger machine type to handle global traffic.

Question 14

A team has deployed a model on Vertex AI and wants to cache frequent identical prediction requests to improve latency and reduce cost. Which Google Cloud service should they use?

Accepted Answer

Cloud Memorystore. Cloud Memorystore provides a managed Redis or Memcached in-memory cache that can store frequent identical prediction requests and their responses, dramatically reducing latency and backend load. It is the standard Google Cloud service for application-level caching in front of Vertex AI endpoints.

Answer

Cloud Bigtable

Answer

Cloud CDN

Answer

Cloud SQL

Question 15

A company has a CI/CD pipeline that retrains a model every time new training data is available. They want to automatically deploy the new model to production only if it passes a set of evaluation tests on a staging environment. Which approach best implements this?

Accepted Answer

Implement a two-stage pipeline: train and deploy to staging, run evaluation tests, and if passed, deploy to production using conditional logic.. The correct approach is a two-stage pipeline: train and deploy to a staging environment, run automated evaluation tests against the staged model, and use conditional logic to promote to production only if the tests pass. This implements a proper ML CI/CD gate that prevents regressions from reaching production.

Answer

Use Cloud Build to trigger a training job and then a separate deployment job without evaluation.

Answer

Use a single Vertex AI pipeline that trains and deploys to staging, then manually promote.

Answer

Train and deploy directly to production in one pipeline.