PDE Operationalizing machine learning models Practice Test 5 — 15 Questions

Question 1

Refer to the exhibit. A Cloud Build step fails when pushing a Docker image to Artifact Registry. What is the missing IAM role for the Cloud Build service account?

Accepted Answer

roles/artifactregistry.writer. The Cloud Build service account needs the `roles/artifactregistry.writer` role to push Docker images to Artifact Registry. This role grants the necessary permissions to upload artifacts, including images, to the registry. Without it, the build step fails with an authorization error.

Answer

roles/containerregistry.admin

Answer

roles/storage.objectCreator

Answer

roles/cloudbuild.builds.editor

Question 2

You have deployed a classification model on Vertex AI Endpoints. The model's training data had a balanced class distribution, but over time, the production data has shifted such that one class appears 90% of the time. The model's overall accuracy remains high, but the recall for the minority class has dropped significantly. What is the best approach to detect and address this issue?

Accepted Answer

Set up Vertex AI Model Monitoring to detect skew and drift, and retrain using a sliding window of recent data. Vertex AI Model Monitoring is specifically designed to detect skew and drift between training and serving data. In this scenario, the production data has shifted to 90% of one class, which is a clear case of data drift. By setting up monitoring, you can be alerted to this drift and then retrain the model using a sliding window of recent data, which adapts to the new distribution without requiring full retraining on the entire historical dataset. This approach directly addresses the root cause—the shift in class distribution—rather than just treating symptoms.

Answer

Retrain the model daily on the entire historical dataset

Answer

Increase the number of replicas on the endpoint to reduce latency

Answer

Adjust the decision threshold to improve minority class recall

Question 3

A data science team has trained a TensorFlow model for image classification and wants to deploy it to production with minimal latency. They have already exported the model as a SavedModel directory. Which service should they use to create an online prediction endpoint?

Accepted Answer

Vertex AI Endpoints. Vertex AI Endpoints is the correct service for deploying a TensorFlow SavedModel to an online prediction endpoint with minimal latency. It provides managed, autoscaling infrastructure optimized for real-time inference, including GPU/TPU support, request batching, and automatic health checking, which are essential for production deployment.

Answer

Cloud Functions

Answer

AI Platform Prediction (legacy)

Answer

Cloud Dataflow

Question 4

Your Vertex AI model deployed on an endpoint is experiencing high tail latency during online predictions. The model uses a large embedding layer, and the input size varies. You have enabled automatic scaling with a minimum of 2 replicas and maximum of 10. What is the most likely cause of the latency spikes and the best first step to diagnose?

Accepted Answer

The endpoint's target CPU utilization might be set too low, causing rapid scale-down and cold starts. Check Cloud Logging for scaling events.. High tail latency with variable input sizes and a large embedding layer often points to cold starts from aggressive scaling. When the target CPU utilization is set too low, the endpoint scales down quickly during lulls, and a subsequent burst of requests forces new replicas to spin up, causing latency spikes. Checking Cloud Logging for scaling events is the best first step because it directly reveals whether the endpoint is scaling down and then experiencing cold starts.

Answer

The model's SavedModel is too large due to the embedding layer; reduce embedding dimensions to lower latency.

Answer

The model uses a custom prediction routine that is not optimized; use tf.function to improve performance.

Answer

Enable model monitoring for online prediction and add a buffer to the endpoint's machine type.

Question 5

You run batch predictions using Vertex AI Batch Prediction on a tabular dataset. The job processes 1 million rows and takes 6 hours to complete. You need to reduce the processing time to under 2 hours without increasing cost significantly. What should you do?

Accepted Answer

Increase the machine count (number of worker replicas) in the batch prediction job. Correct: D. Increasing the number of worker replicas speeds up batch jobs. Option A is wrong because machine type may help but usually less effective than parallelization. Option B is wrong because streaming is for online. Option C is wrong because reducing data size is not an option.

Answer

Switch to a machine type with more CPU cores and vCPUs

Answer

Downsample the dataset to 500k rows

Answer

Use Online prediction instead of batch

Question 6

Your team uses Vertex AI Feature Store to serve features for online predictions. A feature value changes frequently (e.g., user session clicks). Which type of feature should you use to ensure low-latency writes and reads?

Accepted Answer

Streaming feature. Correct: A. Streaming features are for high-frequency updates. Option B is wrong because batch features are for static data. Option C is wrong because Vertex AI doesn't have 'feature view' as a type. Option D is wrong because Bigtable is not a feature store feature.

Answer

Batch feature

Answer

Feature view

Answer

Bigtable-backed feature

Question 7

You have two versions of a classification model (v1 and v2) deployed on a Vertex AI Endpoint. You want to gradually roll out v2 to 10% of traffic, monitor performance, and if metrics are better, increase traffic to 100%. You have set up model monitoring for skew and drift. Which configuration should you use?

Accepted Answer

Use the Vertex AI Endpoint 'traffic_split' parameter to assign 10% of traffic to v2 and 90% to v1.. The Vertex AI Endpoint 'traffic_split' parameter allows you to direct a percentage of inference requests to different model versions deployed on the same endpoint. Setting 10% to v2 and 90% to v1 enables a gradual rollout while monitoring skew and drift, and you can adjust the split as needed. This is the native, supported method for canary deployments in Vertex AI, avoiding the complexity and latency of external load balancers.

Answer

Deploy v2 to a separate endpoint and use a load balancer to route 10% of traffic.

Answer

Create a new deployment with v2 on the same endpoint and set the 'min_replica_count' to 1 for both versions.

Answer

Enable Vertex AI Model Monitoring on the endpoint and set up alerting for performance drop.

Question 8

You need to automate retraining of a model when new training data becomes available every week. The training pipeline runs on Vertex AI Pipelines and is triggered by Cloud Composer. After retraining, you want to evaluate the new model against a golden dataset. If the model's accuracy improves by at least 1%, it should be automatically deployed to the staging endpoint. What is the best way to implement the decision logic?

Accepted Answer

Add a conditional step in the Vertex AI Pipeline to evaluate the model and deploy if the accuracy improvement threshold is met.. Option B is correct because Vertex AI Pipelines supports conditional execution natively via the `Condition` component, allowing you to evaluate the new model's accuracy against the golden dataset within the same pipeline and deploy only if the improvement threshold (≥1%) is met. This approach keeps the entire retraining, evaluation, and deployment workflow automated, auditable, and tightly coupled within a single orchestrated pipeline, avoiding external triggers or manual steps.

Answer

Use Cloud Functions to compare metrics and call the endpoint if conditions are met.

Answer

After training, run a batch prediction job on the golden dataset and compare metrics manually.

Answer

Use Vertex AI Experiments to log metrics and set up an alert to manually deploy.

Question 9

Your team wants to continuously monitor a deployed model's performance in production. They need to detect when the model's predictions become unreliable due to changes in the real world (e.g., new customer behavior). Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct choice because it is specifically designed to continuously track a deployed model's prediction quality over time, detecting issues like data drift, feature drift, and prediction skew that indicate the model's reliability is degrading due to changes in the real world. It automatically compares incoming prediction data against a baseline training dataset and alerts when statistical distributions shift beyond configurable thresholds, enabling proactive retraining or intervention.

Answer

Vertex AI Explainable AI

Answer

Vertex AI Experiments

Answer

Vertex AI Prediction

Question 10

A model deployed on Vertex AI Endpoint is making predictions with high accuracy but the business team suspects bias against a certain demographic group. You need to analyze the model's predictions for fairness. What is the most effective approach?

Accepted Answer

Use Vertex AI Explainable AI to generate feature attributions for each prediction and analyze whether the demographic feature has disproportionate impact.. Vertex AI Explainable AI provides per-instance feature attributions, which allow you to examine how the model uses each feature—including sensitive demographic attributes—to arrive at a prediction. By analyzing these attributions across demographic groups, you can detect whether the model disproportionately relies on the demographic feature, indicating potential bias. This approach is more granular than aggregate metrics and directly addresses the business team's concern about bias in individual predictions.

Answer

Compute overall fairness metrics by comparing prediction rates across demographic groups.

Answer

Collect more data for the under-represented group and retrain the model.

Answer

Use Vertex AI Model Monitoring to check for training-serving skew on the demographic feature.

Question 11

Which TWO best practices should be followed when managing multiple model versions on Vertex AI Endpoints for a production system?

Accepted Answer

Use traffic splitting to gradually shift traffic to a new version while monitoring performance.. Option C is correct because Vertex AI Endpoints support traffic splitting, allowing you to route a percentage of inference requests to a new model version while the rest goes to the existing version. This enables gradual rollout, monitoring of performance metrics (e.g., latency, error rate), and safe rollback without downtime. It is a best practice for production systems to validate a new version before fully cutting over.

Answer

Always keep all historical versions deployed to enable fast rollback.

Answer

If two versions share the same endpoint, they must have exactly the same machine type.

Answer

Use the same endpoint for multiple versions and adjust min_replica_count, max_replica_count for each version.

Question 12

Which TWO metrics are most important to monitor for a real-time online prediction system to ensure service reliability and model performance?

Accepted Answer

Prediction latency (p50, p99). Prediction latency (p50, p99) is critical because it directly impacts user experience and system reliability; high tail latency (p99) can indicate resource contention or model complexity issues. Prediction error rate (4xx/5xx) is essential for detecting serving infrastructure failures, such as model server crashes or misconfigured endpoints, which degrade service reliability. Both metrics provide real-time visibility into the serving layer's health and performance, distinct from offline training metrics.

Answer

Feature distribution skew between training and serving

Answer

Number of training examples used for the latest model version

Answer

Batch prediction job throughput

Question 13

Which THREE components are typically part of a Vertex AI Pipeline for automated model retraining and deployment?

Accepted Answer

Training component (e.g., CustomContainerTrainingJob). Option C is correct because a training component, such as a `CustomContainerTrainingJob`, is the core step in a Vertex AI Pipeline that executes the model training logic. It defines the container image, machine configuration, and hyperparameters, enabling automated retraining when triggered by a schedule or event.

Answer

Cloud Monitoring alerting component

Answer

Cloud Storage artifact storage component

Question 14

You run `gcloud ai models describe` and get the error above. The model was created successfully from a training job that completed without errors. The model ID is correct. What is the most likely cause?

Accepted Answer

The model was created in a different region (e.g., europe-west4) than the one specified in the command.. Option D is correct because `gcloud ai models describe` defaults to the `us-central1` region unless overridden with the `--region` flag. If the model was created in a different region (e.g., `europe-west4`), the command will fail with a 'Model not found' error even though the model ID is correct. Vertex AI models are regional resources, so the region must match exactly.

Answer

The model was deleted or expired due to time-to-live settings.

Answer

The gcloud command is not authenticated to the correct project.

Answer

The model was created but not yet trained; training must complete before describe works.

Question 15

A user named Charlie needs to deploy a model to a Vertex AI Endpoint and also create training jobs. Which role should be assigned to Charlie?

Accepted Answer

roles/aiplatform.user. Correct: B. aiplatform.user includes permissions to create models and deploy endpoints. Option A is wrong because modelUser is read-only for predictions. Option C is wrong because editor includes unrelated permissions. Option D is wrong because owner is too broad.

Answer

roles/owner

Answer

roles/aiplatform.modelUser

Answer

roles/editor

PDE Operationalizing machine learning models Practice Test 5 — 15 Questions

Exhibit

PDE Operationalizing machine learning models Practice Test 5 — 15 Questions

Exhibit