PMLE Practice Test 46 — 15 Questions

Question 1

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. 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

Question 2

Which TWO practices help ensure reproducible ML experiments?

Accepted Answer

Use Vertex AI Experiments to track parameters and metrics. Vertex AI Experiments automatically logs parameters, metrics, and artifacts for each run, creating a complete lineage that enables exact reproduction of results. By tracking these details alongside the code version, you can recreate the exact environment and configuration that produced a given outcome, which is essential for reproducibility.

Answer

Store all artifacts in a temporary bucket

Answer

Use a random seed for each run

Answer

Use preemptible VMs

Question 3

A retail company wants to build a customer churn prediction model using BigQuery ML. The data is stored in BigQuery tables and includes customer demographics, purchase history, and support interactions. The data scientist wants to experiment with different model types quickly without moving data to another environment. Which approach should they use?

Accepted Answer

Use BigQuery ML to create and evaluate models directly in BigQuery.. BigQuery ML (BQML) allows data scientists to create, train, and evaluate machine learning models directly in BigQuery using SQL, without moving data to another environment. This approach supports rapid experimentation with various model types (e.g., logistic regression, boosted trees, deep neural networks) and is ideal for the stated requirement of quick iteration while keeping data in place.

Answer

Use Cloud Composer to orchestrate a custom training pipeline on Vertex AI.

Answer

Use AI Platform Notebooks with pandas and scikit-learn.

Answer

Export the data to Cloud Storage and use Vertex AI AutoML Tables.

Question 4

An e-commerce company uses a recommendation model deployed on Vertex AI Endpoints. The model's latency increases gradually over two weeks, causing timeouts. The model is served using a custom container. What is the most likely root cause and corrective action?

Accepted Answer

The custom container has a memory leak; implement memory monitoring and set container resource limits.. A gradual increase in latency over two weeks, without a sudden spike, strongly indicates a memory leak in the custom container. As the leak accumulates, the container's garbage collection becomes less effective, leading to increased GC pauses and eventual timeouts. Setting resource limits and monitoring memory usage can prevent the container from exhausting host memory and causing performance degradation.

Answer

The model is receiving more traffic; scale the number of replicas.

Answer

The Vertex AI endpoint has changed its URL; update the client application.

Answer

The model file size has grown due to feature engineering; reduce feature count.

Question 5

A retail company deploys a new recommendation model alongside the current champion on Vertex AI Endpoints. They want to gradually shift traffic to the challenger while monitoring business metrics (conversion rate). Which two steps are required? (Choose 2)

Accepted Answer

Deploy the challenger model to the same endpoint as the champion with a separate deployed model.. Option C is correct because Vertex AI Endpoints support multiple deployed models behind a single endpoint, so the challenger must be deployed as a separate DeployedModel (with its own model ID and deployed_model_id) on the same endpoint as the champion to enable side-by-side serving. Option D is correct because gradual traffic shifting is implemented by setting the endpoint's traffic_split map, which assigns percentage weights keyed by deployed_model_id (e.g., champion deployed model: 90, challenger deployed model: 10), allowing controlled canary rollout. Option A is not required because Vertex AI Experiments tracks training/experiment runs and metrics, not live endpoint traffic splitting. Option B is not required because Memorystore is a caching layer and does not perform traffic splitting or model comparison. Option E, while useful for observing conversion rate, is not a required step to shift traffic and is not marked correct; the question asks for the steps needed to perform the gradual shift itself.

Answer

Use Vertex AI Experiments to track the traffic split percentages.

Answer

Enable Cloud Memorystore to cache identical requests for both models.

Answer

Use Cloud Monitoring to track custom metrics like conversion rate per model version.

Question 6

A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

Accepted Answer

Git integration in Vertex AI Workbench. Option A, Git integration in Vertex AI Workbench, is correct because user-managed notebooks include built-in Git support, allowing data scientists to clone repositories, commit, push, and pull notebook code directly from the JupyterLab interface for version control and collaboration. Option E, Cloud Source Repositories, is correct because it is a fully managed private Git repository service on Google Cloud that can host the team's notebook code and integrate with Workbench's Git tooling for sharing and versioning. Option B, Cloud Functions, is incorrect because it is a serverless compute service for event-driven functions, not a version control or code-sharing tool. Option C, Vertex AI Model Registry, is incorrect because it manages trained model versions and their metadata, not notebook source code. Option D, Vertex AI Experiments, is incorrect because it tracks experiment runs, parameters, and metrics, not Git-based notebook version control.

Answer

Cloud Functions

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 7

What is the primary purpose of Vertex AI Edge Manager?

Accepted Answer

To deploy and manage ML models on edge devices at scale. Vertex AI Edge Manager is specifically designed to deploy, monitor, and manage ML models on edge devices at scale. It handles model packaging, over-the-air updates, and health monitoring across fleets of edge devices, which is distinct from simply running batch predictions or converting model formats.

Answer

To run batch predictions on edge devices

Answer

To convert models to TensorFlow Lite automatically

Answer

To train models on edge devices using federated learning

Question 8

A media company wants to automatically moderate user-uploaded videos by detecting explicit content (e.g., violence, adult material). They need a solution that integrates with their video processing pipeline and scales to millions of videos. Which approach should they take?

Accepted Answer

Use Video Intelligence API with explicit content detection. Video Intelligence API provides explicit content detection specifically designed to identify violence, adult material, and other explicit content in videos. It is a managed service that scales automatically and can be integrated into video processing pipelines via its API. This is the most direct and scalable solution for moderating user-uploaded videos.

Answer

Use AutoML Video to train a custom explicit content detection model

Answer

Use Natural Language API on video transcripts

Answer

Use Vision API to analyze each video frame

Question 9

You are an ML engineer at a fintech company. You have a prototype credit risk model built using XGBoost that achieves high accuracy on historical data. The model is trained on a dataset with 500,000 rows and 50 features. The company wants to deploy this model to production to score loan applications in real-time. The production environment must handle a peak load of 100 requests per second with a latency under 200ms. You have decided to use Vertex AI for deployment. After deploying the model as a Vertex AI endpoint with a single n1-standard-4 machine, you notice that latency exceeds 500ms at peak load and some requests time out. You have verified that the model prediction itself (excluding network overhead) takes about 50ms on average. What should you do to meet the latency and throughput requirements?

Accepted Answer

Enable autoscaling with a minimum of 2 replicas and use a larger machine type (e.g., n1-standard-8) to handle more concurrent requests.. The latency bottleneck is not the model inference time (50ms) but the inability of a single n1-standard-4 machine to handle 100 concurrent requests per second without queuing. By enabling autoscaling with a minimum of 2 replicas and upgrading to n1-standard-8, you increase both the number of concurrent requests the endpoint can process and the CPU/memory resources per replica, reducing queue wait times and keeping total latency under 200ms. This directly addresses the throughput and latency requirements without changing the model or switching to batch processing.

Answer

Change the machine type to a GPU-accelerated machine like n1-standard-4 with a T4 GPU.

Answer

Prune the model to reduce size and improve prediction speed.

Answer

Switch from online prediction to batch prediction using Vertex AI Batch Prediction.

Question 10

A model deployed on a Vertex AI Endpoint uses an image model with XRAI explainability. The team notices that the prediction distributions are shifting over time. They want to monitor prediction drift. However, the explainability feature is not enabled. What must the engineer do to enable monitoring prediction drift?

Accepted Answer

Configure Vertex AI Model Monitoring to monitor prediction drift. Vertex AI Model Monitoring is the managed service that detects prediction drift (and training-serving skew) by comparing live prediction distributions against a baseline. It operates independently of explainability features — XRAI is only for feature attribution, not drift detection. Enabling Model Monitoring on the endpoint is the correct, purpose-built action to monitor prediction drift.

Answer

Re-deploy the model with a sampling rate of 100%

Answer

Enable Vertex AI Explainability with XRAI on the endpoint deployment

Answer

Enable request/response logging to BigQuery and build custom drift detection

Question 11

A company uses a custom container on Vertex AI Prediction. They want to send custom metrics from their prediction container to Cloud Monitoring. Which method should they use?

Accepted Answer

OpenCensus or OpenTelemetry SDK. OpenCensus and OpenTelemetry are the recommended open-source frameworks for exporting custom metrics from custom containers on Vertex AI Prediction to Cloud Monitoring. They provide a standardized way to instrument your application code, collect metrics, and send them directly to Cloud Monitoring via the Cloud Monitoring API, without requiring additional agents or log-based workarounds.

Answer

Vertex AI built-in metrics

Answer

Stackdriver Monitoring agent installed in the container

Answer

Cloud Logging log-based metrics

Question 12

A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store centralizes feature storage, ensuring the same features are used for training and serving, reducing training-serving skew.

Answer

Vertex AI Workbench

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 13

A machine learning engineer is using Vertex AI Pipelines and wants to run a custom Python function as a component. They need to pass a dataset artifact from a previous component and output a model artifact. Which decorator should they use to define the component in the Kubeflow Pipelines SDK v2?

Accepted Answer

@dsl.component. The correct decorator is @dsl.component because in Kubeflow Pipelines SDK v2, this decorator is used to define a custom Python function as a reusable pipeline component. It automatically handles input and output artifact serialization, such as passing a dataset artifact from a previous component and outputting a model artifact, by leveraging the component's type annotations and the KFP artifact system.

Answer

@dsl.task

Answer

@dsl.pipeline

Answer

@dsl.container

Question 14

You are deploying a PyTorch model on Vertex AI using a custom container with NVIDIA Triton Inference Server. The model is a large transformer that requires GPU. You want to optimize GPU utilization and reduce memory footprint. Which technique should you apply?

Accepted Answer

Apply model quantization using TensorRT.. Model quantization using TensorRT reduces the precision of model weights (e.g., from FP32 to FP16 or INT8), which directly decreases GPU memory usage and can improve throughput by enabling faster arithmetic operations on compatible NVIDIA GPUs. This technique is specifically designed to optimize GPU utilization and memory footprint for large transformer models deployed with Triton Inference Server.

Answer

Enable dynamic batching in Triton.

Answer

Use CPU-only instances to avoid GPU memory issues.

Answer

Increase the number of GPU replicas.

Question 15

Your team has a production ML model on Vertex AI that shows a gradual decline in accuracy over the past week. The model is retrained weekly using the latest data. Which monitoring approach should you implement to detect the issue earlier?

Accepted Answer

Configure Vertex AI Model Monitoring to detect feature drift and alert when metrics exceed thresholds.. Vertex AI Model Monitoring is designed to detect data drift and concept drift by comparing the distribution of incoming prediction requests against a baseline (e.g., training data). A gradual decline in accuracy over a week strongly suggests feature drift—the input data distribution has shifted away from what the model was trained on. By configuring drift thresholds and alerts, you can catch the drift before it significantly degrades model performance, enabling earlier retraining or investigation.

Answer

Create a Cloud Monitoring alert for prediction response count.

Answer

Use BigQuery ML to retrain the model more frequently.

Answer

Set up a Cloud Monitoring uptime check on the prediction endpoint.