PDE Operationalizing machine learning models Practice Test 7 — 15 Questions

Question 1

An MLOps team wants to implement continuous deployment of ML models using Cloud Build and Vertex AI. They have a GitHub repository with training code. What should they use?

Accepted Answer

Cloud Build trigger with a custom step to run Vertex AI Training job and deploy. Option A is correct: Cloud Build triggers can include custom steps to run Vertex AI Training jobs and then deploy the model. Option B is wrong because Vertex AI Pipelines is an orchestration service, not a CI/CD system; Cloud Build is the CI/CD tool. Option C is wrong because Cloud Functions is event-driven but not designed for CI/CD pipelines. Option D is wrong because Cloud Run is for serverless containers, not for training and deploying ML models.

Answer

Deploy using Cloud Run

Answer

Vertex AI Pipelines integrated with Cloud Build

Answer

Cloud Functions to monitor GitHub

Question 2

A company has a model that requires GPU for inference and has strict latency requirements. They deployed on Vertex AI Endpoint with autoscaling but observe cold start latency when scaling up. What is the best solution?

Accepted Answer

Set a higher min_replica_count to keep instances warm. Option A is correct: setting a higher min_replica_count ensures there are always some warm instances ready to serve traffic, reducing cold start latency. Option B is wrong because a larger GPU does not address the cold start issue. Option C is wrong because batch prediction is not suitable for online serving. Option D is wrong because pre-compiling with TensorRT can improve inference speed but does not eliminate cold start delays from scaling.

Answer

Pre-compile the model with TensorRT

Answer

Use a larger GPU instance

Answer

Switch to batch prediction

Question 3

A data scientist wants to test a new model version on a small percentage of traffic before full rollout. Which Vertex AI feature allows this?

Accepted Answer

Endpoint traffic splitting. Vertex AI Endpoint traffic splitting allows you to route a specified percentage of inference requests to different model versions deployed on the same endpoint. This enables gradual rollout by directing a small fraction of traffic (e.g., 5%) to the new model while the rest goes to the current version, without needing separate endpoints or manual routing logic.

Answer

A/B testing

Answer

Model monitoring

Answer

Model versioning with canary deployments

Question 4

After deploying a model, the team notices that predictions are significantly different from training data distribution. What should they do?

Accepted Answer

Set up Vertex AI Model Monitoring for skew detection. Vertex AI Model Monitoring is specifically designed to detect skew between training data and serving data, including prediction drift. When predictions differ significantly from the training distribution, this indicates a skew or drift issue that Model Monitoring can alert on, enabling proactive investigation. Updating the endpoint or retraining without diagnosis would not address the root cause, and reviewing the pipeline alone does not provide ongoing detection.

Answer

Update the model endpoint

Answer

Review the training data pipeline

Answer

Retrain the model with new data

Question 5

A company uses Vertex AI Feature Store for serving features. They have a high-throughput online serving requirement. Which configuration should they use?

Accepted Answer

Vertex AI Feature Store with online serving enabled. Vertex AI Feature Store with online serving enabled is the correct choice because it is specifically designed for low-latency, high-throughput retrieval of feature values for online predictions. It uses a managed Bigtable backend optimized for real-time serving, ensuring consistent performance under high request loads without requiring manual infrastructure management.

Answer

Cloud Storage with high-memory instances

Answer

Bigtable as serving source

Answer

Firestore

Question 6

A company deploys an ML model using Vertex AI Pipelines. They want to ensure reproducibility and traceability. Which TWO practices should they implement?

Accepted Answer

Pin all dependency versions. Pinning all dependency versions (Option A) ensures that every pipeline run uses the exact same library versions, eliminating variability from package updates. This is a fundamental practice for reproducibility because even a minor version bump can change model behavior or break code. In Vertex AI Pipelines, dependencies are typically specified in a `requirements.txt` or `Dockerfile`, and pinning them (e.g., `tensorflow==2.12.0`) guarantees consistent execution environments across runs.

Answer

Record dataset version using Vertex AI Dataset

Answer

Use custom containers for every step

Answer

Use Kubeflow Pipelines instead

Question 7

A team needs to optimize online prediction cost for a model that has unpredictable traffic spikes. Which TWO strategies are most effective?

Accepted Answer

Enable autoscaling with a low min_replica_count and high max_replica_count. Option A is correct because autoscaling with a low min_replica_count and high max_replica_count allows the deployment to handle unpredictable traffic spikes by dynamically adjusting the number of replicas. This ensures cost efficiency during low traffic while providing capacity to scale out rapidly when demand surges, a key requirement for online prediction serving.

Answer

Set up Model Monitoring to trigger scaling

Answer

Deploy the model on a single high-memory machine

Answer

Use batch prediction during high traffic

Question 8

A company wants to implement a robust MLOps lifecycle on Google Cloud. Which THREE components are essential?

Accepted Answer

Vertex AI Model Registry for versioning. Vertex AI Model Registry is essential for versioning because it provides a centralized repository to track, manage, and deploy different versions of trained ML models. This ensures reproducibility, auditability, and the ability to roll back to previous versions, which is critical for a robust MLOps lifecycle.

Answer

Pub/Sub for event-driven retraining

Answer

Cloud SQL for model metadata

Question 9

Refer to the exhibit. What is the most likely cause of the error?

Accepted Answer

The model artifact was not uploaded to Cloud Storage. The error occurs because the model artifact must be uploaded to Cloud Storage before it can be deployed to an endpoint. Vertex AI requires the model to be stored in a Cloud Storage bucket, and the deployment process references that artifact. Without the artifact in Cloud Storage, the endpoint creation or model deployment fails with an error indicating the resource is missing.

Answer

The endpoint does not exist

Answer

The service account lacks permissions

Answer

The model ID is invalid

Question 10

Refer to the exhibit. The feature store 'my_fs' responds to offline queries but online serving requests fail. What is the most likely cause?

Accepted Answer

Update the existing feature store to enable online serving. The feature store 'my_fs' responds to offline queries but not online serving requests, which indicates that online serving is not enabled for the feature store. In Vertex AI Feature Store, online serving requires a dedicated endpoint and underlying infrastructure (e.g., Bigtable) to serve low-latency requests. Updating the existing feature store to enable online serving (option C) is the correct fix, as it activates the necessary serving resources without recreating the store.

Answer

Create a new feature store with online serving enabled

Answer

Use Cloud Bigtable directly

Answer

Re-import features into a new store

Question 11

Refer to the exhibit. What is the most likely cause?

Accepted Answer

The model container does not support this prediction route. Option D is correct: the error 'model type is not supported for this prediction method' suggests the model container does not support the online prediction route (e.g., it expects a different protocol or is designed for batch only). Option A is wrong because the endpoint ID is likely correct; the error is model-specific. Option B is wrong because the request format might be correct but the model rejects it. Option C is wrong because batch-only models would show a different error; this error indicates the model's container doesn't handle the request.

Answer

The request format is incorrect

Answer

The model was built for batch prediction only

Answer

The endpoint ID is wrong

Question 12

A startup is deploying a machine learning model for real-time fraud detection. They need low latency and automatic scaling during peak hours. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Endpoints. Vertex AI Endpoints is the managed service for online predictions with autoscaling, ideal for real-time low-latency requirements.

Answer

Cloud Functions

Answer

Batch Prediction on Vertex AI

Answer

Cloud AI Platform Prediction with custom containers

Question 13

A team has trained a model using AutoML Tables. They want to deploy it for batch predictions on a schedule. What is the simplest approach?

Accepted Answer

Use Vertex AI Batch Prediction with a scheduled pipeline. Vertex AI Batch Prediction is the simplest approach because it is a managed service that directly supports batch predictions on AutoML Tables models without requiring additional infrastructure. By wrapping it in a scheduled Vertex AI pipeline, you can automate the entire workflow—triggering predictions on a schedule, handling input/output to Cloud Storage, and managing compute resources—all within the Vertex AI ecosystem, minimizing operational overhead.

Answer

Write a Cloud Function triggered by Cloud Scheduler

Answer

Export model to Cloud Storage and use Dataflow

Answer

Deploy to App Engine

Question 14

A data engineer needs to monitor model performance over time for drift detection. What tool is specifically designed for this?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is specifically designed to detect prediction drift and feature skew in deployed machine learning models. It continuously analyzes serving data against training data distributions and alerts when statistical metrics (e.g., Jensen-Shannon divergence, L-infinity distance) exceed configured thresholds, making it the correct tool for drift detection in the context of operationalizing ML models.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

BigQuery ML

Question 15

A machine learning pipeline uses Vertex AI Pipelines. One component fails intermittently due to resource constraints. What is the best way to handle this?

Accepted Answer

Use retry policies in the component specification. Option A is correct because Vertex AI Pipelines supports retry policies at the component level via the `retry` field in the component specification (YAML or Python). This allows the pipeline to automatically re-execute a failed component when the failure is transient (e.g., resource exhaustion), without manual intervention. Retry policies are the standard mechanism for handling intermittent failures in a serverless orchestration environment like Vertex AI Pipelines.

Answer

Deploy the pipeline on a larger cluster

Answer

Increase the pipeline timeout

Answer

Use a different orchestrator