PMLE Practice Test 11 — 15 Questions

Question 1

A team has set up the IAM policy above on a Vertex AI project. Alice, a data scientist, reports that she cannot create a Vertex AI Training custom job using a pre-built container. Other data scientists in the group 'data-scientists@example.com' have the same issue. What is the most likely cause?

Accepted Answer

The 'roles/aiplatform.user' role does not grant the permission to create custom training jobs.. The 'roles/aiplatform.user' role includes the 'aiplatform.customJobs.create' permission, which is required to create custom training jobs. However, the issue is that Alice and her group cannot create a custom job using a pre-built container. The most likely cause is that the 'roles/aiplatform.user' role does not grant the permission to create custom training jobs with pre-built containers; it only allows using managed models or notebooks. To create custom training jobs, the 'roles/aiplatform.customJobUser' role is needed, which includes the necessary permissions for custom job creation.

Answer

The Vertex AI Custom Code Service Agent service account is missing the 'roles/aiplatform.user' role.

Answer

Alice is not included in the 'data-scientists@example.com' group.

Answer

The service account 'vertex-ai@project.iam.gserviceaccount.com' does not have permission to access the training data.

Question 2

A team deployed a prototype classification model to Vertex AI Prediction. After a week, they notice the metrics shown in the exhibit. What is the most likely cause of the performance degradation and latency increase?

Accepted Answer

The training data does not represent the current production data distribution, causing the model to make incorrect predictions and requiring more computation.. The exhibit shows both accuracy degradation and increased latency. Option C is correct because when the production data distribution shifts away from the training data (data drift), the model makes more incorrect predictions, which can trigger additional computation (e.g., retries, fallback logic, or increased uncertainty estimation) and cause latency spikes. Vertex AI Prediction does not inherently add computation for wrong predictions, but the model's internal confidence thresholds or post-processing steps may consume extra resources when handling out-of-distribution inputs.

Answer

The prediction endpoint's autoscaling is too slow, causing requests to queue and time out.

Answer

The prediction requests are too large, exceeding the maximum request size limit for Vertex AI.

Answer

The custom prediction container uses outdated libraries that are incompatible with Vertex AI's runtime.

Question 3

A data science team is deploying a PyTorch model for real-time inference using Vertex AI Endpoints. The model requires a custom container with specific CUDA drivers and Python packages. They have created a Docker image and pushed it to Artifact Registry. The pipeline should automatically retrain the model every week and deploy the new version if it passes validation. However, the deployment step fails intermittently with the error 'The container image is not compatible with the machine type.' What is the most likely cause?

Accepted Answer

The container image requires GPU support but the machine type specified in the endpoint is a CPU-only machine.. The error 'The container image is not compatible with the machine type' indicates a mismatch between the container's hardware requirements and the machine type selected for the Vertex AI Endpoint. Since the custom container requires specific CUDA drivers, it is built for GPU acceleration. If the endpoint is configured with a CPU-only machine type (e.g., n1-standard-4), the container will fail to run because the GPU drivers cannot initialize, triggering this incompatibility error.

Answer

The service account does not have permission to pull the container from Artifact Registry.

Answer

The container's health check endpoint is not responding correctly.

Answer

The model artifact size exceeds the maximum allowed for the machine type.

Question 4

A data science team deploys a regression model to predict house prices. After one month, the mean absolute error (MAE) on the serving data increases by 20% compared to the test set. Which monitoring strategy should the team implement first to diagnose the issue?

Accepted Answer

Monitor prediction residuals and compute serving-time MAE over sliding windows.. Option B is correct because the first step in diagnosing a 20% MAE increase on serving data is to monitor prediction residuals over sliding windows. This directly tracks how model errors evolve in production, allowing the team to detect whether performance degradation is sudden or gradual, and to correlate it with specific time windows or data slices. Computing serving-time MAE on sliding windows provides an immediate, interpretable signal of model health without assuming the root cause.

Answer

Retrain the model daily with the latest data to adapt to changing patterns.

Answer

Compare the distribution of training labels with serving labels using a two-sample t-test.

Answer

Monitor input feature distributions for drift using the Kolmogorov-Smirnov test.

Question 5

A financial services company wants to detect fraudulent transactions in real-time. They have a trained XGBoost model that runs on a single Compute Engine instance. The current solution processes about 100 transactions per second, but they need to scale to 10,000 transactions per second. Which approach should they take?

Accepted Answer

Deploy the model to Vertex AI Prediction with autoscaling enabled. Vertex AI Prediction with autoscaling is the correct choice because it is purpose-built for serving ML models at scale, automatically adjusting the number of compute nodes based on incoming request traffic. This allows the company to seamlessly handle the increase from 100 to 10,000 transactions per second without manual intervention, while XGBoost is natively supported as a framework.

Answer

Increase the VM to a machine type with more vCPUs and memory

Answer

Use Dataflow to process transactions in micro-batches every second

Answer

Rewrite the model as a Cloud Function triggered by Pub/Sub messages

Question 6

A manufacturing company wants to predict equipment failure using sensor data. The data is highly imbalanced (only 1% failures). They are using a gradient boosted tree model with class weights. The model achieves 0.99 recall but 0.2 precision on the test set. Which two actions should they take to improve precision without significantly hurting recall? (Choose TWO)

Accepted Answer

Try an anomaly detection algorithm like Isolation Forest. Option B is correct because anomaly detection algorithms like Isolation Forest are designed to identify rare events by isolating anomalies rather than modeling the majority class, which can improve precision when the minority class is extremely rare (1%). Option E is correct because increasing the decision threshold for classifying a positive reduces false positives by requiring higher confidence for a positive prediction, directly improving precision while only minimally reducing recall if the model's probability scores are well-calibrated.

Answer

Oversample the minority class using SMOTE

Answer

Add more features to the model

Answer

Increase the class weight for the minority class

Question 7

A media company uses a custom Python script on a Compute Engine VM to run batch predictions with a large ML model. The script loads the model from Cloud Storage, processes records from a Pub/Sub pull subscription, and writes results to BigQuery. Predictions are taking too long and the VM often runs out of memory. Which two changes should the company implement to improve performance and scalability? (Choose TWO)

Accepted Answer

Change Pub/Sub to a push subscription that sends messages to a load-balanced group of VMs. Option B is correct because switching to a push subscription with a load-balanced group of VMs distributes the message processing load across multiple instances, preventing any single VM from being overwhelmed. This directly addresses the memory exhaustion issue by parallelizing the work and allowing horizontal scaling.

Answer

Deploy the model on Vertex AI Prediction for batch prediction

Answer

Switch to a larger VM with more memory

Answer

Store results in Cloud SQL instead of BigQuery

Question 8

A company is deploying a machine learning model for real-time inference on Vertex AI. Which TWO practices improve serving performance and reliability?

Accepted Answer

Enable autoscaling to handle traffic variations.. Option B is correct because Vertex AI's autoscaling dynamically adjusts the number of replicas based on incoming request traffic, ensuring low latency during spikes and cost savings during lulls. This is critical for real-time inference, where consistent response times are required and manual scaling would either over-provision or under-provision resources. Autoscaling uses metrics like CPU utilization or request count to scale up or down, directly improving serving performance and reliability.

Answer

Use batch prediction for all requests.

Answer

Use manual scaling with a fixed number of replicas.

Answer

Deploy all models on the same machine type for consistency.

Question 9

A data science team has trained a large deep learning model using Vertex AI Workbench. They want to deploy it to Vertex AI Prediction for online serving. The model is stored in a custom container with a Python-based web server. Which TWO actions should the team take to ensure optimal performance and cost?

Accepted Answer

Request GPU machine types for the prediction nodes.. B is correct because deep learning models, especially large ones, benefit significantly from GPU acceleration for online inference due to their parallel processing capabilities. Vertex AI Prediction supports GPU machine types, and using them reduces latency and improves throughput for compute-intensive model serving, which is critical for optimal performance.

Answer

Configure the model to use a larger batch size for inference.

Answer

Set the container's health check path to '/predict'.

Answer

Use a global load balancer to distribute traffic across regions.

Question 10

A company serves a PyTorch model using a custom container on Vertex AI Prediction. They notice that after a few hours, the endpoint returns 502 errors. The logs show 'Out of memory' errors. The container has a memory limit of 4GB, and the model loads a 3GB vocabulary file. What is the most likely cause and best fix?

Accepted Answer

Load the vocabulary file once at startup and reuse it.. The 502 errors and 'Out of memory' errors indicate that the container is running out of memory during inference. Since the model loads a 3GB vocabulary file, and the container has only 4GB of memory, loading this file repeatedly for each prediction request (e.g., inside the prediction handler) would quickly exhaust memory. The correct fix is to load the vocabulary file once at container startup and reuse it across all requests, which is a standard best practice for serving models with large static assets.

Answer

Increase the container memory to 8GB.

Answer

Increase the number of replicas to distribute load.

Answer

Switch to Vertex AI Batch Prediction.

Question 11

A Vertex AI pipeline is triggered from Cloud Build using the configuration above. The pipeline fails with an error: 'Unable to submit build: The source code is not available.' What is the most likely cause?

Accepted Answer

The 'gcloud builds submit' command does not have access to the source code in the Cloud Build environment.. The error 'Unable to submit build: The source code is not available' indicates that the Cloud Build environment cannot locate the source code when the 'gcloud builds submit' command is executed. This typically happens when the pipeline is triggered from Cloud Build but the source code is not properly staged or accessible in the build context, often because the build configuration does not include the source directory or the source is not uploaded to Cloud Storage. Option B correctly identifies that the command lacks access to the source code in the Cloud Build environment.

Answer

The Docker build step failed silently due to a missing dependency.

Answer

The Docker image tag does not include a hash, causing the push to fail.

Answer

The Cloud Build service account lacks permission to access the Vertex AI Pipeline API.

Question 12

A non-technical user wants to build a binary classification model using Vertex AI. Which UI should they use?

Accepted Answer

Vertex AI AutoML. Vertex AI AutoML is the correct choice because it provides a no-code graphical user interface specifically designed for non-technical users to build, train, and deploy machine learning models, including binary classification models, without writing any code. It automates the entire ML pipeline—feature engineering, model selection, hyperparameter tuning—allowing users to simply upload labeled data and get a production-ready model.

Answer

Vertex AI Workbench

Answer

Vertex AI Pipelines

Answer

Vertex AI Prediction

Question 13

Which TWO strategies help ensure data consistency when multiple teams are contributing features to a shared Vertex AI Feature Store?

Accepted Answer

Define and enforce feature schemas using the Feature Store API.. Option C is correct because defining and enforcing feature schemas using the Vertex AI Feature Store API ensures that all teams adhere to a consistent data structure (e.g., fixed feature names, data types, and value ranges). This prevents schema drift and ingestion conflicts, which are common when multiple teams independently push features to the same feature store. Without schema enforcement, one team might inadvertently change a feature's data type or add unexpected values, breaking downstream models.

Answer

Each team should create their own feature store to avoid conflicts.

Answer

Use only batch ingestion to keep features synchronized.

Answer

Allow each team to independently define feature engineering logic.

Question 14

A team is using Vertex AI Experiments to compare different hyperparameters. They want to automatically record the hyperparameters. What is the correct way?

Accepted Answer

Use the `aiplatform.start_run()` context manager. Option B is correct because Vertex AI Experiments provides a native `aiplatform.start_run()` context manager that automatically captures hyperparameters passed as key-value arguments, logging them to the experiment run metadata without manual intervention. This integrates directly with the Vertex AI SDK, ensuring consistency and traceability across runs.

Answer

Manually log to console

Answer

Write to a CSV file

Answer

Use BigQuery

Question 15

A company has multiple teams working on different models. They want to enforce consistent data preprocessing steps across all teams. Which approach should they take?

Accepted Answer

Create shared Vertex AI Pipelines components. Vertex AI Pipelines components allow teams to define reusable, versioned, and parameterized preprocessing steps that can be shared across models and pipelines. This ensures consistent execution of data transformations because each component encapsulates the exact code and environment, and pipelines enforce the same DAG of steps regardless of which team triggers them.

Answer

Use Cloud Composer to orchestrate preprocessing

Answer

Write shared Python packages in Artifact Registry

Answer

Use Cloud Dataflow templates

PMLE Practice Test 11 — 15 Questions

Exhibit

PMLE Practice Test 11 — 15 Questions

Exhibit