PMLE Practice Test 45 — 15 Questions

Question 1

A data science team is using a shared Cloud Storage bucket to store training data. Multiple team members are simultaneously uploading new data files, and occasionally the wrong version of a file is used in training, leading to inconsistent results. Which best practice should the team implement to ensure data version consistency?

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

Accepted Answer

Enable object versioning on the Cloud Storage bucket and use the version ID when referencing data files.. Enabling object versioning on a Cloud Storage bucket preserves each object's history, allowing the team to reference a specific version ID when reading data files. This ensures that every training run uses the exact same version of a file, eliminating inconsistency from concurrent uploads. The version ID acts as an immutable pointer, decoupling the training process from the bucket's live state.

Answer

Use Cloud Composer to schedule a daily snapshot of the Cloud Storage bucket.

Answer

Migrate all training data to BigQuery and use time-travel queries to access historical versions.

Answer

Restrict write access to the bucket to only one team member using IAM roles.

Question 2

A machine learning engineer needs to share a trained model with the product team for integration. The model is stored in Cloud Storage, and the product team’s service account needs read access. The engineer wants to follow the principle of least privilege. Which IAM configuration should be used?

Accepted Answer

Grant the product team's service account the roles/storage.objectViewer role at the bucket level.. Granting the product team's service account the roles/storage.objectViewer role at the bucket level provides read-only access to objects in that specific bucket, adhering to the principle of least privilege. This role allows the service account to list and read objects without granting broader permissions, such as modifying or deleting them, and scoping it to the bucket prevents unnecessary access to other buckets in the project.

Answer

Generate a signed URL with read access and share it with the product team.

Answer

Grant the product team's service account the roles/storage.objectAdmin role at the bucket level.

Answer

Grant the product team's service account the roles/storage.objectViewer role at the project level.

Question 3

A company is migrating from an on-premises ML serving infrastructure to Vertex AI. They have multiple models that need to be served from the same endpoint with different traffic percentages. They also need to monitor prediction quality. Which THREE actions should they take? (Choose 3)

Accepted Answer

Deploy multiple model versions on the same endpoint with traffic_split parameter.. Vertex AI endpoints support deploying multiple model versions and using the `traffic_split` parameter to distribute traffic percentages among them. This allows the company to serve different models from a single endpoint while controlling the proportion of requests each model receives, meeting the requirement for a unified serving infrastructure.

Answer

Deploy each model as a separate endpoint and use Cloud Load Balancing.

Answer

Export logs to BigQuery for manual analysis only.

Question 4

A team is monitoring a batch prediction job on Vertex AI. Which two metrics should they monitor to ensure the job completes successfully without errors?

Accepted Answer

Job failure rate. The job failure rate directly indicates whether the batch prediction job is completing successfully or encountering errors. Monitoring this metric allows the team to detect and respond to failures in the prediction pipeline, ensuring the job finishes without errors.

Answer

Data size of input

Answer

Prediction requests per second

Answer

Model endpoint latency

Question 5

A data scientist trained a custom TensorFlow model using Vertex AI Training and wants to deploy it for online predictions with low latency (<100ms). Which deployment option on Google Cloud is best?

Accepted Answer

Deploy on Vertex AI Endpoints. Vertex AI Endpoints is the correct choice because it is purpose-built for deploying TensorFlow models with optimized serving infrastructure, including automatic scaling, GPU/TPU support, and built-in monitoring for latency-sensitive online predictions. It provides a managed endpoint that can achieve sub-100ms latency by leveraging model optimization techniques like TensorFlow Serving and hardware accelerators, which are not available in the other options.

Answer

Deploy on Cloud Run with a custom container

Answer

Deploy on Cloud Functions

Answer

Deploy on AI Platform Prediction (legacy)

Question 6

A company deploys a model to Vertex AI Prediction with autoscaling enabled. During a flash sale, traffic spikes 10x, but the endpoint fails to scale fast enough, causing high latency. What is the most likely cause and solution?

Accepted Answer

The min_nodes setting is too low; increase min_nodes to handle baseline traffic. With Vertex AI Prediction autoscaling, the `min_nodes` setting defines the baseline number of instances that are always kept running. During a flash sale, traffic spikes 10x, but if `min_nodes` is set too low, the autoscaler cannot provision new instances quickly enough to handle the sudden load, resulting in high latency. Increasing `min_nodes` ensures a sufficient baseline capacity to absorb the initial spike while the autoscaler scales up additional nodes.

Answer

Switch to preemptible VMs to reduce cost and allow more instances

Answer

The model container is too large; rebuild with a smaller image

Answer

Use Cloud Functions to pre-warm instances before the sale

Question 7

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 can detect feature drift and training-serving skew, which are common causes of accuracy decline. By alerting when drift metrics exceed thresholds, the team can identify issues before they significantly impact performance. Option B is incorrect; prediction response count is a volume metric and does not reflect model quality. Option C is incorrect; BigQuery ML retraining is not a monitoring solution. Option D is incorrect; uptime checks only verify endpoint availability, not accuracy.

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.

Question 8

A company needs to detect objects in live video streams from security cameras. They require low-latency predictions and want to minimise operational overhead. Which TWO services should they use? (Choose 2)

Accepted Answer

Cloud Run. Video Intelligence API can analyse live streams with object detection. Cloud Run provides serverless compute to host the streaming pipeline. AutoML Video requires custom training, Vertex AI Predictions is for deployed models, Live Stream API is for ingestion.

Answer

Live Stream API

Answer

Vertex AI Predictions

Answer

AutoML Video

Question 9

A team has a trained TensorFlow model running locally and wants to deploy it for low-latency online predictions on Google Cloud. Which service should they use?

Accepted Answer

Vertex AI Prediction. Vertex AI Prediction is the correct choice because it is a fully managed service designed specifically for deploying trained ML models for online (real-time) prediction with low latency. It supports importing TensorFlow SavedModel artifacts and automatically scales the serving infrastructure, including GPU/TPU support, to handle request traffic while providing built-in monitoring and explainability features.

Answer

AI Platform Training

Answer

Cloud Run

Answer

Cloud Functions

Question 10

An ML team wants to run a hyperparameter tuning job on Vertex AI using a pre-built pipeline component. Which component should they use?

Accepted Answer

HyperparameterTuningJobRunOp. The HyperparameterTuningJobRunOp is the correct pre-built Vertex AI pipeline component specifically designed to launch a hyperparameter tuning job. It wraps the Vertex AI HyperparameterTuningJob API, allowing you to specify the worker pool spec, metric target, and parameter specifications directly within a Kubeflow Pipelines (KFP) or Vertex AI Pipelines orchestration context.

Answer

AutoMLTabularTrainingJobRunOp

Answer

CustomTrainingJobRunOp with hyperparameter arguments.

Answer

ModelTrainComponent

Question 11

A company has multiple teams that need to access and manage ML models in Vertex AI. Different teams require different permission levels: the data science team should be able to create and update models, while the MLOps team should have full control. What is the recommended approach to manage access?

Accepted Answer

Use folders in Google Cloud Resource Manager and assign IAM roles at the folder level. Google Cloud Resource Manager folders allow hierarchical IAM policy inheritance, enabling you to assign roles like 'roles/aiplatform.user' (for data science) and 'roles/aiplatform.admin' (for MLOps) at the folder level. This approach scales across multiple projects within the folder, ensuring consistent permissions without per-project duplication. It aligns with the principle of least privilege and centralized access management for Vertex AI resources.

Answer

Grant the 'aiplatform.user' role to a Google Group containing all users

Answer

Use labels and tags on models to control access

Answer

Create a separate Google Cloud project for each team

Question 12

You have a Vertex AI endpoint that serves a model for real-time predictions. You want to update the model to a new version with zero downtime. Which approach should you take?

Accepted Answer

Deploy the new model version to the same endpoint and then set traffic to 100% for the new version.. Vertex AI endpoints support canary deployments by allowing you to deploy a new model version to the same endpoint and then gradually shift traffic to it using the `traffic_split` parameter. Setting traffic to 100% for the new version after deployment ensures zero downtime, as the endpoint remains active and serves requests from the old version until the switch is complete.

Answer

Delete the endpoint and recreate it with the new model.

Answer

Use Cloud Load Balancing to switch traffic between two endpoints.

Answer

Create a new endpoint and update the client application to point to the new endpoint.

Question 13

A company is evaluating Google Cloud ML solutions. Which TWO services are appropriate for building custom machine learning models (not using pre-built APIs)? (Choose TWO.)

Accepted Answer

Vertex AI Workbench. Vertex AI Workbench is correct because it provides a Jupyter-based development environment where data scientists can write custom code, train models from scratch, and manage the entire ML workflow without relying on pre-built APIs. It supports custom containers, frameworks like TensorFlow and PyTorch, and integrates with Vertex AI Training for distributed training.

Answer

Cloud Translation API

Answer

Cloud AutoML

Answer

Cloud Vision API

Question 14

A company is deploying a complex model that requires GPU for inference. They want to use Vertex AI for serving. Which TWO steps are required to deploy the model with GPU support? (Choose 2)

Accepted Answer

Select a GPU-enabled machine type such as n1-standard-4 with 1 x NVIDIA Tesla T4.. Vertex AI requires selecting a GPU-enabled machine type (e.g., n1-standard-4 with 1 x NVIDIA Tesla T4) when deploying a model for inference. This is done in the machine specification of the endpoint deployment, ensuring the GPU hardware is allocated for the serving container.

Answer

Enable Vertex AI Model Optimization for automatic GPU compilation.

Answer

Increase the minimum replicas to at least 2 for GPU redundancy.

Answer

Use gRPC protocol for prediction requests to reduce latency.

Question 15

A company uses AutoML Tables (Vertex AI AutoML for tabular data) to predict customer churn. Their dataset has 10,000 rows and 50 features. During training, they notice the model's performance is poor. Which approach is most likely to improve the model?

Accepted Answer

Enable automatic feature engineering transformations. AutoML Tables (Vertex AI AutoML for tabular data) includes automatic feature engineering transformations such as scaling, one-hot encoding, and feature cross creation. These transformations are essential for capturing non-linear relationships and interactions between features, which can significantly improve model performance when the default preprocessing is insufficient. Enabling this option directly addresses the poor performance by allowing the model to learn more complex patterns from the data.

Answer

Switch to BigQuery ML linear regression

Answer

Increase the training budget to 10 node hours

Answer

Remove 20 features to reduce noise