PDE Operationalizing machine learning models Practice Test 9 — 15 Questions

Question 1

A machine learning team wants to deploy a new model version for canary testing, where only 5% of traffic is routed to the new version. Which Vertex AI endpoint configuration supports this?

Accepted Answer

Configure the endpoint with traffic split: 95% to old version, 5% to new version.. Vertex AI supports traffic splitting between model versions in endpoints. Option B is correct. Option A is wrong because it deploys a separate endpoint. Option C is wrong because it suggests direct traffic control by client. Option D is wrong because A/B testing is a process, not configuration.

Answer

Have the client application randomly select which model to call with 5% probability.

Answer

Deploy the new version to a separate endpoint and direct 5% of users via a load balancer.

Answer

Use an A/B testing framework outside of Vertex AI to compare results.

Question 2

A company needs to serve predictions for a model that runs an expensive computation on each request. The model is used by a batch job that processes millions of records each night, and also by a real-time API for a few thousand queries per hour. Which prediction strategy minimizes cost and latency for both use cases?

Accepted Answer

Use Vertex AI batch prediction for the nightly job and a separate online endpoint with auto-scaling for the real-time API.. Using batch prediction for the batch job and a separate online endpoint with a smaller machine or auto-scaling for real-time queries optimizes cost and latency. Option D is correct. Option A is wrong because batch prediction alone doesn't serve real-time. Option B is wrong because online endpoint for millions of records is expensive. Option C is wrong because using the same endpoint for both may cause interference.

Answer

Deploy two identical models, one on a Compute Engine VM for batch, one on Vertex AI for online, and synchronize updates.

Answer

Use Vertex AI batch prediction for both workloads.

Answer

Use a single online Vertex AI endpoint with auto-scaling to handle both workloads.

Question 3

A data scientist has iterated on a model and produced a new version. The organization requires the ability to roll back to the previous version quickly if the new version performs poorly in production. Which approach should be used?

Accepted Answer

Upload both versions to Vertex AI Model Registry and use endpoint traffic splitting to route 100% to the safe version if needed.. Vertex AI Model Registry allows you to deploy multiple model versions and use endpoint traffic splitting to gradually shift traffic or instantly route 100% to a specific version. This enables immediate rollback by setting the traffic split to 100% for the previous model version without redeploying or changing infrastructure.

Answer

Store each model version in a separate Cloud Storage bucket.

Answer

Keep the previous model in a container image and redeploy via Cloud Run.

Answer

Use Cloud Source Repositories to tag model versions.

Question 4

A Cloud Build pipeline is set up to train a model on Vertex AI. The build fails with the error: 'ERROR: (gcloud.ai-platform.jobs.submit.training) NOT_FOUND: The parent project does not exist.' The project ID and the service account are correctly configured. What is the most likely cause?

Accepted Answer

The Cloud Build service account does not have the aiplatform.jobs.create permission on the project.. The error 'NOT_FOUND: The parent project does not exist' indicates that the Cloud Build service account lacks the necessary IAM permission to submit a training job to Vertex AI. Even though the project ID and service account are correctly configured, the Cloud Build service account must have the 'aiplatform.jobs.create' permission (or the 'Vertex AI User' role) on the project. Without this, the API call fails because the service account is not authorized to access the project resource.

Answer

The region specified for the training job does not exist.

Answer

The training job requires a GPU, which is not available in the specified region.

Answer

The training package is not uploaded to Cloud Storage before the pipeline runs.

Question 5

A company serves multiple models using Vertex AI endpoints. Each model has different latency and memory requirements. To minimize cost, the company wants to share underlying compute resources among models. Which approach should they use?

Accepted Answer

Deploy all models to a single Vertex AI endpoint and configure traffic splitting.. Vertex AI endpoints support traffic splitting, allowing you to deploy multiple models behind a single endpoint and route a percentage of traffic to each model. This enables resource sharing and cost optimization because the underlying compute infrastructure is shared among the models, unlike separate endpoints which would each require dedicated resources.

Answer

Deploy each model as a separate Cloud Run service and use a load balancer.

Answer

Use a single GKE cluster with multiple deployments and use Istio for routing.

Answer

Create separate endpoints for each model and use a load balancer to route traffic.

Question 6

Which TWO are benefits of using Vertex AI Endpoints for model serving?

Accepted Answer

Integrated monitoring for prediction latency and error rates.. Vertex AI Endpoints provide integrated monitoring for prediction latency and error rates out of the box, enabling you to track model performance and detect anomalies without additional instrumentation. This is a core operational feature that helps maintain service-level objectives (SLOs) and quickly identify degradation in production.

Answer

Batch prediction support out of the box.

Answer

Automatic model retraining when drift is detected.

Answer

Built-in support for A/B testing without any additional configuration.

Question 7

Which THREE steps are required to set up a continuous training pipeline on Google Cloud using Vertex AI?

Accepted Answer

Create a Vertex AI Pipeline to orchestrate data preprocessing, training, and model evaluation.. Option B is correct because Vertex AI Pipelines provide a managed, repeatable, and scalable way to orchestrate the entire ML workflow, including data preprocessing, training, and model evaluation. This is essential for a continuous training pipeline, as it automates the sequence of steps and ensures consistency across runs.

Answer

Run training on a single Compute Engine VM with a cron job.

Answer

Manually upload the model to Vertex AI Model Registry after each training run.

Question 8

Which TWO are common causes of prediction bias in a deployed machine learning model in production?

Accepted Answer

Data drift between training and serving data distributions.. Option B is correct because data drift refers to changes in the statistical properties of the input features between the training and serving environments. When the distribution of real-world data shifts (e.g., seasonal trends, user behavior changes), the model's predictions become biased even if the model itself hasn't changed. This is a primary cause of prediction bias in production ML systems.

Answer

Model accuracy is too high.

Answer

Model is overfitted to training data.

Answer

Low latency predictions.

Question 9

A company needs to deploy a trained model for real-time predictions with low latency. Which Vertex AI resource should they use?

Accepted Answer

Vertex AI Endpoints. Vertex AI Endpoints are designed for online prediction, providing a managed service that hosts models for real-time inference with low latency. They automatically scale resources and handle traffic routing, making them the correct choice for deploying a trained model that needs to respond to individual prediction requests quickly.

Answer

Cloud TPU

Answer

Vertex AI Batch Prediction

Answer

Cloud Run

Question 10

A data engineer wants to automatically detect when the distribution of input features to a production model has shifted significantly. Which Vertex AI feature should they enable?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct service because it is specifically designed to continuously detect feature distribution drift and prediction skew in production models. It automatically compares the current input feature distribution against a baseline (e.g., training data) and triggers alerts when significant statistical shifts occur, enabling proactive retraining or investigation.

Answer

Vertex AI Vizier

Answer

Vertex AI Explainable AI

Answer

Vertex AI Feature Store

Question 11

A team has multiple versions of a model and wants to manage them centrally, including tracking metadata and promoting versions to production. Which tool should they use?

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry is designed for managing model versions, metadata, and deployment. Cloud Storage is storage only. BigQuery is for analytics. GitHub is for source code.

Answer

Cloud Storage

Answer

BigQuery

Answer

GitHub

Question 12

A production model deployed on Vertex AI Endpoint is experiencing high latency during traffic spikes. The current configuration uses a single replica. What is the most efficient solution?

Accepted Answer

Enable autoscaling with minReplicaCount=1 and maxReplicaCount=10. Enabling autoscaling with a min replica count ensures always-on capacity and scales up during spikes. Using a larger machine type might help but is less dynamic. Using batch prediction doesn't solve real-time latency. Increasing min replicas without autoscaling leaves resources idle at quiet times.

Answer

Set a higher min replica count (e.g., 3)

Answer

Use a larger machine type (e.g., n1-highmem-8)

Answer

Switch to batch prediction to handle spikes

Question 13

A company wants to automate model retraining and deployment whenever new training data becomes available. Which service should be used to orchestrate the end-to-end workflow?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the correct choice because it is a managed service specifically designed to orchestrate and automate end-to-end ML workflows, including model retraining and deployment triggered by new data. It allows you to define pipelines as a directed acyclic graph (DAG) of steps using the Kubeflow Pipelines SDK or pre-built components, and it integrates natively with other Vertex AI services for training, evaluation, and deployment.

Answer

Cloud Build

Answer

Cloud Scheduler

Answer

Cloud Composer

Question 14

A data scientist needs to provide explanations for each prediction made by a deployed autoML model to comply with regulatory requirements. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI Explainable AI. Vertex AI Explainable AI is the correct feature because it provides feature attributions and explanations for each prediction, enabling compliance with regulatory requirements that demand interpretability. It uses techniques like Shapley value approximations or integrated gradients to quantify the contribution of each input feature to the model's output, which is essential for auditing and transparency in deployed autoML models.

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Vizier

Answer

Vertex AI Feature Store

Question 15

A company runs large batch prediction jobs on Vertex AI every day. They want to minimize costs while ensuring the jobs complete within a 4-hour window. The model requires significant memory. What is the most cost-effective approach?

Accepted Answer

Use preemptible VMs with a machine type that meets memory requirements. Preemptible VMs (now called Spot VMs) are significantly cheaper than standard VMs (up to 60-80% discount) and are ideal for fault-tolerant batch prediction jobs that can handle interruptions. Since the job has a 4-hour window and the model requires significant memory, using preemptible VMs with a machine type that meets the memory requirements minimizes cost while allowing the job to complete if restarted within the time limit.

Answer

Use Cloud TPUs to accelerate predictions

Answer

Use a smaller machine type (e.g., n1-standard-4) to reduce cost

Answer

Use standard VMs and reduce parallelization