PDE Operationalizing machine learning models Practice Test 1 — 15 Questions

Question 1

A company deploys a machine learning model to Vertex AI for real-time predictions. After deployment, they notice that prediction latency spikes during peak traffic hours. Which approach should they take to reduce latency without sacrificing accuracy?

Accepted Answer

Configure auto-scaling with higher min and max instances. Option A is correct because configuring auto-scaling with higher min and max instances ensures that Vertex AI has sufficient pre-warmed replicas to handle traffic spikes without cold-start latency. This approach maintains model accuracy because it does not alter the model architecture or inference logic, only the infrastructure capacity.

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Reduce the number of input features

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Switch from online to batch prediction

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Use a larger machine type for the model

Question 2

A data science team uses Vertex AI Pipelines to automate retraining. They want to ensure that only models with performance above a threshold are deployed. Which component should they add to the pipeline?

Accepted Answer

Vertex AI Model Evaluation. Vertex AI Model Evaluation provides built-in evaluation metrics and threshold-based validation that can be used as a pipeline condition to gate model deployment. By adding a Model Evaluation component, the pipeline can compare model performance against a predefined threshold and only proceed to deploy if the metrics (e.g., AUC, precision, recall) meet or exceed the required value.

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Vertex AI Feature Store

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Cloud Build trigger

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Cloud Monitoring alert

Question 3

A company trains a custom model using TensorFlow and wants to deploy it to Vertex AI for low-latency predictions. The model is large (2 GB). Which deployment option should they choose?

Accepted Answer

Deploy to Vertex AI Endpoint with a custom container. Option C is correct because deploying a large (2 GB) model to Vertex AI Endpoint with a custom container allows you to package the model, its dependencies, and a serving framework (e.g., TensorFlow Serving) into a Docker image. This approach supports low-latency predictions by keeping the model loaded in memory across requests, and it can scale to handle real-time inference traffic, unlike batch or serverless options that have cold-start or size limitations.

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Use Vertex AI Batch Prediction job

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Deploy as a Cloud Function

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Deploy to Cloud Run with minimum instances

Question 4

A company uses Vertex AI to serve a model. They notice that some predictions are incorrect due to data drift. What is the best way to detect and retrain the model automatically?

Accepted Answer

Use Vertex AI Model Monitoring with alerts and retraining pipeline. Option D is correct because Vertex AI Model Monitoring is specifically designed to detect data drift and feature skew in production models. It can be configured to send alerts and trigger an automated retraining pipeline via Cloud Functions or Vertex AI Pipelines, enabling continuous model improvement without manual intervention. This directly addresses the need for automatic detection and retraining in response to data drift.

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Store predictions in BigQuery and run scheduled queries

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Create a Cloud Monitoring dashboard

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Set up Cloud Logging metrics to monitor predictions

Question 5

A financial services company needs to explain predictions from a complex ensemble model for regulatory compliance. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Explainable AI. Vertex AI Explainable AI is the correct service because it provides feature attributions and other explainability techniques (e.g., Shapley value approximations, integrated gradients) that help interpret predictions from complex ensemble models. This is essential for regulatory compliance, where the company must demonstrate how input features influence each prediction, ensuring transparency and auditability.

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Vertex AI Vizier

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Vertex AI Feature Store

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Vertex AI Prediction

Question 6

A team wants to retrain a model weekly using new data stored in BigQuery. They want to minimize manual effort. Which approach should they use?

Accepted Answer

Create a Vertex AI Pipeline scheduled via Cloud Scheduler. Vertex AI Pipelines allow you to define a repeatable, automated ML workflow that can be triggered on a schedule via Cloud Scheduler. This minimizes manual effort by handling data extraction from BigQuery, model retraining, and deployment without human intervention, while also providing versioning and monitoring capabilities.

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Use Cloud Scheduler to trigger a Cloud Function that retrains

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Retrain manually in a notebook each week

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Use Cloud Composer to orchestrate retraining

Question 7

A company deploys a model to Vertex AI Endpoint. They want to run a canary deployment to test a new model version with 10% of traffic. How should they configure this?

Accepted Answer

Deploy the new model to the same endpoint and set traffic split. Option C is correct because Vertex AI Endpoints natively support traffic splitting between model versions deployed to the same endpoint. By deploying the new model version to the same endpoint and setting a traffic split of 10% to the new version and 90% to the current version, the company can perform a canary deployment without changing the application code or infrastructure.

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Deploy to a new endpoint and update the application to call both

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Use Cloud Load Balancing to route traffic

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Deploy to Cloud Run and use gradual rollout

Question 8

A data scientist uses Vertex AI Workbench notebooks for model development. They want to share the environment with team members while maintaining version control. Which approach should they use?

Accepted Answer

Use a user-managed notebook instance with multiple users. A user-managed notebook instance with multiple users is the correct approach because Vertex AI Workbench supports collaboration by allowing multiple users to access the same instance via IAM permissions, while the underlying Git integration enables version control. This setup provides a shared, persistent environment where team members can work on the same codebase without duplicating work, and changes can be tracked through Git repositories.

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Use Cloud Shell and clone the repo

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Share the notebook via Cloud Storage

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Store notebooks in Cloud Source Repositories

Question 9

A company wants to monitor the performance of a deployed model in production. Which metric indicates that the model's predictions are degrading?

Accepted Answer

Increase in prediction error rate. An increase in prediction error rate directly indicates that the model's outputs are deviating from the expected or ground-truth values, signaling degradation in predictive performance. This metric captures the core concept of model drift, where the statistical properties of the input data or the relationship between features and labels change over time, leading to less accurate predictions. In production ML monitoring, tracking error rate (e.g., classification accuracy, RMSE) is the primary method to detect when a model needs retraining or updating.

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Increase in prediction latency

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Decrease in throughput

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Increase in number of requests

Question 10

A team uses Vertex AI AutoML Tables to train a model. They need to deploy the model for real-time predictions with high availability. Which deployment configuration should they use?

Accepted Answer

Deploy to a Vertex AI Endpoint with multiple replicas and auto-scaling. For real-time predictions with high availability, you need a deployment that can handle traffic spikes and failover. Deploying to a Vertex AI Endpoint with multiple replicas and auto-scaling ensures that the model is served from multiple instances, providing redundancy and the ability to scale up or down based on demand. This configuration meets the high-availability requirement by distributing load and automatically recovering from instance failures.

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Export as a Cloud Function

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Deploy to a Vertex AI Endpoint with 1 replica

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Use a Vertex AI Batch Prediction job

Question 11

A company uses Vertex AI to serve a model that requires GPU for inference. They want to minimize cost while handling variable traffic. Which strategy should they use?

Accepted Answer

Use a Vertex AI Endpoint with GPU and configure auto-scaling to zero when idle. Option B is correct because Vertex AI Endpoints support GPU-accelerated inference with autoscaling, including the ability to scale down to zero replicas when there is no traffic. This minimizes cost by only incurring GPU charges during active inference, while still handling variable traffic through dynamic scaling.

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Deploy the model to Cloud Functions with GPU

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Use Vertex AI Batch Prediction with GPU

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Use a Vertex AI Endpoint with GPU with a fixed number of replicas

Question 12

Which TWO steps are required to deploy a custom scikit-learn model to Vertex AI for online predictions?

Accepted Answer

Save the model using joblib or pickle. Option C is correct because scikit-learn models must be serialized using joblib or pickle to be saved as a model artifact that can be uploaded to Vertex AI. Vertex AI's pre-built prediction containers for scikit-learn expect the model file to be in this format (typically model.joblib or model.pkl) to serve online predictions.

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Write a custom prediction routine

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Containerize the model using Docker

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Create a Vertex AI Endpoint manually

Question 13

Which THREE factors should be considered when designing a Vertex AI Pipeline for continuous training?

Accepted Answer

Cost of training and infrastructure. Cost of training and infrastructure (A) is correct because Vertex AI Pipelines incur compute costs for each pipeline run, including training, data processing, and orchestration. Continuous training amplifies these costs, so you must consider budget constraints, resource optimization (e.g., using preemptible VMs), and cost monitoring to avoid unexpected bills.

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Debugging tools like Cloud Debugger

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Number of model versions to keep

Question 14

Which TWO actions can help reduce prediction latency for a Vertex AI endpoint?

Accepted Answer

Optimize the model architecture to reduce size. Optimizing the model architecture to reduce size directly decreases the computational load during inference, which lowers prediction latency. Smaller models require fewer floating-point operations (FLOPs) per prediction, enabling faster response times on Vertex AI endpoints.

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Increase the number of features

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Use a larger machine type with more vCPUs

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Set min replicas to 0 to save cost

Question 15

Which THREE metrics should be monitored for a deployed machine learning model in production?

Accepted Answer

Prediction error rate. Prediction error rate (Option B) is a direct measure of model accuracy in production, reflecting how often the model's predictions deviate from actual outcomes. Monitoring this metric is essential for detecting model degradation, data quality issues, or concept drift that can silently reduce model performance over time.

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Number of replicas

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Training time