PMLE Practice Test 40 — 15 Questions

Question 1

A company uses Vertex AI Model Monitoring. Which two configuration options can be set to reduce false positive drift alerts?

Accepted Answer

Use a sample percentage of predictions. Option A (Use a sample percentage of predictions) is correct because sampling only a subset of incoming prediction requests for drift analysis reduces the volume of data evaluated, smoothing out minor statistical fluctuations that would otherwise trigger spurious drift alerts. Option C (Increase the drift threshold) is correct because raising the threshold means the observed drift must be larger before an alert fires, directly filtering out small, benign deviations and lowering false positives. Option B (Set a shorter alerting window) is wrong because a shorter window captures less data and is more sensitive to noise, which typically increases rather than reduces false alerts. Option D (Decrease the drift threshold) is wrong because a lower threshold makes alerts fire on smaller deviations, producing more false positives. Option E (Enable feature attribution monitoring) is wrong because feature attribution explains which features drive predictions; it is an explainability capability, not a mechanism for reducing false positive drift alerts.

Answer

Set a shorter alerting window

Answer

Decrease the drift threshold

Answer

Enable feature attribution monitoring

Question 2

Which THREE considerations are important when setting up a shared feature store in Vertex AI Feature Store for multiple teams?

Accepted Answer

Enable feature monitoring for data quality and freshness. Option A is correct because enabling feature monitoring in Vertex AI Feature Store lets you track data quality and freshness metrics (such as drift and staleness) so that multiple consuming teams can trust the shared features. Option C is correct because a shared feature store requires data governance policies that define who can create, modify, and access features, ensuring consistent standards and compliance across teams. Option D is correct because a feature sharing policy enables cross-team discovery and reuse of features, which is the core purpose of a centralized shared feature store. Option B is not appropriate because using separate BigQuery tables per team fragments the store and undermines the centralized sharing model. Option E is not appropriate because independent ingestion pipelines per team lead to duplicated, inconsistent feature definitions rather than a governed shared store.

Answer

Use separate BigQuery tables for each team's features

Answer

Allow each team to build independent ingestion pipelines

Question 3

A company wants to set up end-to-end monitoring for a Vertex AI model. Which three components should they include?

Accepted Answer

Model performance metrics. For end-to-end monitoring of a Vertex AI model, the company needs to track how well the deployed model is performing, so B (Model performance metrics) is correct because Vertex AI Model Monitoring surfaces metrics like accuracy, precision, and recall against ground-truth or skewed prediction distributions. C (Data drift and concept drift detection) is correct because Vertex AI Model Monitoring specifically detects training-serving skew and drift in feature distributions (data drift) as well as changes in the relationship between features and labels (concept drift), which are core to maintaining model quality over time. D (Prediction latency) is correct because operational monitoring of an endpoint must include request/response latency to ensure the model meets serving SLOs, and Vertex AI exposes these metrics via Cloud Monitoring. The remaining options do not belong: A (Feature store backup status) concerns data durability of a feature store, not model monitoring, and E (Model training cost) is a billing/training concern rather than an end-to-end runtime monitoring component.

Answer

Feature store backup status

Answer

Model training cost

Question 4

An ML engineer needs to track the costs incurred by Vertex AI prediction endpoints. Which tool should they use to set budget alerts and monitor spending?

Accepted Answer

Google Cloud Budgets & Alerts. Google Cloud Budgets & Alerts is the dedicated billing-scoped tool that lets you define a budget amount (or filter by project/service/label) and attach alert thresholds at percentages of actual or forecasted spend. It is the only option that operates on billing data and can trigger Pub/Sub notifications or email alerts when Vertex AI endpoint costs cross a threshold. Model Monitoring, Cloud Logging, and Cloud Monitoring track operational metrics, not monetary spend.

Answer

Vertex AI Model Monitoring

Answer

Cloud Logging

Answer

Cloud Monitoring with custom metrics

Question 5

A logistics company uses Vertex AI AutoML Tables to predict delivery delays based on order attributes, weather data, and traffic data. The model is retrained weekly using a Vertex AI Pipeline that runs a BigQuery query to get training data, then triggers AutoML training. Recently, the pipeline fails with the error 'Dataset not found' when the AutoML training step starts. The BigQuery query runs successfully and outputs a table. Which is the most likely cause?

Accepted Answer

The BigQuery output table is not being passed as a Vertex AI Dataset resource.. The error 'Dataset not found' occurs because AutoML Tables requires a Vertex AI Dataset resource (a metadata wrapper) to reference the training data, not just a BigQuery table. The pipeline's BigQuery query produces a table, but if that table is not explicitly converted into or passed as a Vertex AI Dataset resource (via the `aiplatform.Dataset` creation step), AutoML training cannot locate it. Option D correctly identifies this missing step as the root cause.

Answer

The AutoML training step is referencing a different dataset location.

Answer

The training data has been manually deleted from Cloud Storage.

Answer

The pipeline's IAM permissions are insufficient to access BigQuery.

Question 6

A company has deployed a TensorFlow model on Vertex AI Prediction for real-time inference. They notice that during peak hours, the prediction latency increases significantly, and some requests time out. The model requires GPU acceleration. Which action should they take to reduce latency and avoid timeouts?

Accepted Answer

Enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, and ensure GPU quota is sufficient.. Enabling autoscaling with appropriate min and max replicas allows the endpoint to dynamically scale up during peak traffic and scale down during low traffic, ensuring sufficient GPU resources to handle the load without manual intervention. Ensuring adequate GPU quota is also critical to prevent resource exhaustion. Option B is wrong because switching to a larger machine type with more vCPUs does not address GPU-bound inference latency; the bottleneck is GPU acceleration, not CPU. Option C is wrong because statically increasing replicas leads to over-provisioning and waste during off-peak hours, and cannot react quickly to sudden traffic spikes. Option D is wrong because Cloud Functions are serverless and do not support GPU acceleration; they add additional latency and are not suitable for real-time GPU inference.

Answer

Switch to a larger machine type with more vCPUs.

Answer

Increase the number of replicas in the Vertex AI Prediction endpoint statically to handle peak load.

Answer

Use Cloud Functions to invoke the model asynchronously.

Question 7

A team is building a fraud detection model that requires joining real-time transaction features with historical user features. They need to ensure that the training data does not use future information (data leakage). Which Vertex AI Feature Store capability should they use?

Accepted Answer

Point-in-time correct join. Point-in-time correct joins in Vertex AI Feature Store ensure that when training examples are generated, each row uses only feature values that were valid as of the event timestamp of the label — preventing future data from leaking into training. This is the specific capability designed to avoid label leakage in time-series or event-driven ML. Time travel and online serving do not by themselves guarantee temporal correctness of the join.

Answer

Online store serving with Bigtable

Answer

Feature store time travel

Answer

Feature monitoring for drift

Question 8

Refer to the exhibit. What is the purpose of this query?

Accepted Answer

To find prediction errors in Cloud Logging. The query filters Cloud Logging entries for the string 'prediction failed', which directly indicates prediction errors logged by the ML prediction service. This is a common pattern for monitoring model inference failures in production, not for measuring drift, counting requests, or measuring latency.

Answer

To detect data drift

Answer

To count all prediction requests

Answer

To monitor model latency

Question 9

A data scientist needs to train a large PyTorch model on a custom dataset using Vertex AI. The training script expects data from Cloud Storage and uses GPU acceleration. Which option correctly configures a custom training job with a pre-built container for PyTorch and attaches a single NVIDIA V100 GPU?

Accepted Answer

Use the pre-built container 'us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-12:latest' and in worker_pool_specs set machine_type='n1-standard-4', accelerator_type='NVIDIA_TESLA_V100', accelerator_count=1. Vertex AI custom training jobs use worker_pool_specs to define the machine type, container image, and accelerator configuration. The correct configuration uses the pre-built PyTorch GPU container and specifies machine_type='n1-standard-4', accelerator_type='NVIDIA_TESLA_V100', and accelerator_count=1 in the worker pool spec. This is the documented Vertex AI pattern for attaching a single V100 GPU.

Answer

Use a custom container built from PyTorch base image and specify accelerator_count=1 in the machine spec

Answer

Use the AI Platform Training service with gcloud ai-platform jobs submit training and --scale-tier BASIC_GPU

Answer

Create a training pipeline with AutoML and select GPU runtime

Question 10

A team is scaling a prototype ML model to production on Vertex AI. The model was developed using scikit-learn and requires custom preprocessing. They want to minimize operational overhead and ensure consistency between training and serving. Which approach should they use?

Accepted Answer

Use a pre-built Vertex AI container for scikit-learn and provide a custom training Python package with preprocessing code included.. Using a pre-built Vertex AI container for scikit-learn with a custom training Python package ensures that the same preprocessing code runs during both training and serving, minimizing operational overhead. This approach leverages Vertex AI's managed infrastructure to handle scaling, monitoring, and consistency without requiring custom container maintenance.

Answer

Train on a local machine and upload the model artifacts to Cloud Storage, then create an endpoint with a pre-built container.

Answer

Deploy the model as a custom prediction routine on Vertex AI Endpoints with a custom container.

Answer

Export the model as a .pkl file and use Vertex AI's 'Import Model' with a default container for inference.

Question 11

Refer to the exhibit. A team runs the command above and sees only two models. They know there is a model 'model-v3' created three days ago. What is the most likely reason it is not listed?

Accepted Answer

The model was created in a different region.. The `gcloud ai models list` command lists models within a specific region, as Vertex AI models are regional resources. If 'model-v3' was created in a different region, it would not appear in the output unless the `--region` flag is set to that region. This is the most likely reason the model is missing from the list.

Answer

The model is in a different project.

Answer

The model is not deployed to an endpoint.

Answer

The model's display name contains a hyphen.

Answer

The model was created by a different user.

Question 12

A data scientist wants to automate the retraining of a model when new data arrives in Cloud Storage. Which Google Cloud service is most appropriate for orchestrating this workflow?

Accepted Answer

Cloud Composer. Cloud Composer (D) is the most appropriate service for orchestrating a retraining workflow because it is a fully managed workflow orchestration service built on Apache Airflow. It allows you to define a Directed Acyclic Graph (DAG) that triggers model retraining when new data arrives in Cloud Storage, handling dependencies, scheduling, and monitoring across multiple steps such as data validation, training, and deployment.

Answer

Cloud Run

Answer

Vertex AI Predictions

Answer

Cloud Scheduler

Answer

Cloud Functions

Question 13

A company has a model serving predictions on Vertex AI Endpoints and wants to monitor for prediction drift. They enable Vertex AI Model Monitoring but also need to see a confusion matrix over time. How should they set up the confusion matrix monitoring?

Accepted Answer

Upload ground truth data to BigQuery and use Vertex AI Model Monitoring's model quality monitoring. Vertex AI Model Monitoring's model quality monitoring requires ground truth labels to compute metrics like confusion matrices, precision, recall, and F1. By uploading ground truth to BigQuery and configuring the monitoring job to join predictions with labels, Vertex AI can calculate and display confusion matrices over time. Prediction drift monitoring alone cannot produce a confusion matrix because it lacks label data.

Answer

Use Cloud Monitoring to create a custom dashboard with a confusion matrix chart

Answer

Export predictions to Cloud Storage and run a Dataflow job to compute confusion matrices

Answer

Enable Vertex AI Explainable AI and configure it to output confusion matrices

Question 14

A data analyst wants to build a binary classification model using a low-code ML solution on Google Cloud. The dataset is stored in BigQuery and contains 500,000 rows with 20 features, including categorical and numerical columns. The analyst has minimal coding experience and needs to deploy the model as an API endpoint for real-time predictions. Which two Google Cloud services should the analyst use to accomplish this task with minimal code? Choose two options.

Accepted Answer

Vertex AI Endpoints. Vertex AI Endpoints is correct because it provides a managed service to deploy trained models as REST API endpoints for real-time predictions with minimal code. The analyst can deploy an AutoML Tables model directly to a Vertex AI Endpoint, enabling low-code deployment and serving.

Answer

BigQuery ML

Answer

Cloud Functions

Answer

Vertex AI Workbench

Question 15

An ML engineer is building a pipeline component that takes a dataset URI and a model URI as inputs, and outputs a classification metrics artifact. Which KFP SDK v2 type should the output artifact be annotated with?

Accepted Answer

ClassificationMetrics. In KFP SDK v2, the `ClassificationMetrics` type is specifically designed to output classification metrics such as confusion matrix, ROC curve, and AUC. The question asks for a component that outputs classification metrics, so `ClassificationMetrics` is the correct artifact type. Using `Metrics` would be too generic and not provide the structured schema needed for classification-specific visualizations in the KFP UI.

Answer

Dataset

Answer

Metrics

Answer

Model