PDE Operationalizing machine learning models Practice Test 6 — 15 Questions

Question 1

A user gets the above error when trying to get online predictions. The model was created and the endpoint exists. What is the most likely reason?

Accepted Answer

No version of the model is deployed to the endpoint.. Correct: C. The model must be deployed (a version deployed) to the endpoint. Option A wrong because model exists. Option B wrong because endpoint exists. Option D wrong because region mismatch would give different error.

Answer

The endpoint does not exist.

Answer

The endpoint is in a different region than the model.

Answer

The model does not exist.

Question 2

A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a TensorFlow SavedModel. After deployment, the prediction latency is higher than expected. Which action is most likely to reduce latency without significantly impacting model accuracy?

Accepted Answer

Enable XLA (Accelerated Linear Algebra) compilation on model loading.. Option B is correct because enabling XLA compilation on model load can optimize the computational graph for better performance with no accuracy loss. Options A, C, and D either reduce accuracy or are not applicable.

Answer

Convert the model to TensorFlow Lite and use a smaller model.

Answer

Increase the number of prediction nodes in the AI Platform Prediction cluster.

Answer

Apply quantization to the model weights to reduce size.

Question 3

A data scientist wants to automate retraining of a classification model when new labeled data arrives. The model is deployed on AI Platform Prediction. Which Google Cloud service should be used to orchestrate the retraining pipeline?

Accepted Answer

AI Platform Pipelines. AI Platform Pipelines (now Vertex AI Pipelines) is the correct service because it provides a fully managed, serverless orchestration engine for building, deploying, and running machine learning pipelines. It integrates with Kubeflow Pipelines and TensorFlow Extended (TFX) to automate the retraining workflow when new labeled data arrives, enabling continuous training and model versioning without manual intervention.

Answer

AI Platform Prediction

Answer

AI Platform Continuous Evaluation

Answer

Cloud Dataflow

Question 4

A company runs a real-time fraud detection model using Cloud Dataflow for streaming inference. The model is updated every hour with new training data. The team wants to minimize downtime and ensure that both old and new model versions are available during the update. Which deployment strategy should they use?

Accepted Answer

Canary deployment: deploy the new model alongside the old one, gradually increase traffic to the new model while monitoring.. Canary deployment is the correct strategy because it allows the new model to be deployed alongside the old one, with traffic gradually shifted to the new version while monitoring for errors or performance degradation. This minimizes downtime and ensures both versions are available during the update, which is critical for a real-time fraud detection system where continuous availability and risk mitigation are paramount.

Answer

A/B testing: route a small percentage of traffic to the new model and compare performance.

Answer

Rolling deployment: gradually replace instances of the old model with the new model.

Answer

Blue/green deployment: deploy the new model to a separate endpoint, then switch all traffic at once.

Question 5

A company uses BigQuery ML to create a classification model. The model is used for batch prediction on a weekly basis. After six months, the data distribution shifts, and model accuracy drops. Which approach should the company take to maintain model performance?

Accepted Answer

Schedule automatic retraining of the model using the most recent three months of data.. Option D is correct because the model's accuracy drop is due to data distribution shift (concept drift). Scheduling automatic retraining using the most recent three months of data ensures the model adapts to the new patterns without manual intervention. BigQuery ML supports scheduled queries and automatic model retraining via the `CREATE OR REPLACE MODEL` statement, making this approach both practical and aligned with MLOps best practices for batch prediction pipelines.

Answer

Use Cloud Dataflow to preprocess the data and then update the model with new features.

Answer

Perform hyperparameter tuning on the original training data.

Answer

Apply model quantization to reduce model size and improve inference speed.

Question 6

A team has trained a scikit-learn model and wants to deploy it to AI Platform Prediction for online predictions. What is the required format for the model artifact?

Accepted Answer

A model.joblib file (or model.pkl) along with any custom code.. Option B is correct because AI Platform Prediction expects the model to be saved as joblib or pickle files for scikit-learn. Option A is incorrect because h5 is for Keras; C is for TensorFlow; D is for PyTorch.

Answer

A single .h5 file containing the model weights.

Answer

A SavedModel directory containing the model for TensorFlow.

Answer

A model.pt file for PyTorch models.

Question 7

A real-time recommendation system uses a custom container deployed on AI Platform Prediction. The model requires a large in-memory embedding lookup table that is loaded from Cloud Storage at startup. The current startup time is over 5 minutes, causing prediction requests to timeout. Which strategy would most effectively reduce startup time?

Accepted Answer

Reduce the size of the embedding table by using a smaller embedding dimension or fewer categories.. Option C is correct because cutting down the embedding table size reduces data to load. Option A might not reduce time significantly; B adds complexity; D may not be possible or effective. The core issue is loading a large file, so reduce its size.

Answer

Increase the machine type to one with more memory and CPU.

Answer

Preload the embedding table into a persistent disk and attach it to the container.

Answer

Use a faster storage class for the Cloud Storage bucket, such as Standard instead of Nearline.

Question 8

A data engineering team is building a CI/CD pipeline for machine learning models using Cloud Build and AI Platform. Which TWO practices are essential for ensuring reproducible and safe model deployments?

Accepted Answer

Tag each model version with the Git commit hash of the training code.. Options A and C are correct. A ensures every model version is linked to source and training process; C ensures validation before production. B is not about reproducibility; D might be useful but not essential for reproducibility; E is anti-pattern.

Answer

Use Cloud Functions to trigger retraining on new data arrival.

Answer

Use the same environment for training and serving, possibly via custom containers.

Answer

Directly deploy from the development environment using gcloud commands.

Question 9

A company trains a model using Cloud TPUs. The model is deployed to AI Platform Prediction using a custom container with TensorFlow. Which THREE considerations are most important when serving this model?

Accepted Answer

The serving container must have the same TensorFlow version that was used during training to avoid compatibility issues.. Option B is correct because TensorFlow models are tightly coupled to the specific version of TensorFlow used during training. Serving with a different version can lead to incompatibilities in graph serialization, op definitions, or checkpoint formats, causing runtime errors or silent prediction failures. AI Platform Prediction's custom container must therefore match the training environment's TensorFlow version to ensure the model loads and executes correctly.

Answer

The model should be retrained using GPU to ensure identical performance on serving hardware.

Answer

The model should be quantized to reduce memory footprint before deployment.

Question 10

A team is deploying a TensorFlow model for online predictions on AI Platform Prediction. They want to monitor for data drift and model performance degradation. Which TWO Google Cloud services should they use?

Accepted Answer

AI Platform Continuous Evaluation. AI Platform Continuous Evaluation (option B) is correct because it is a managed service specifically designed to detect data drift and model performance degradation in deployed models. It automatically compares incoming prediction data against the training data distribution and monitors metrics like accuracy over time, triggering alerts when significant drift is detected. Cloud Monitoring (option C) is correct because it provides the underlying metrics and alerting infrastructure that can track model performance indicators (e.g., prediction latency, error rates) and integrate with Continuous Evaluation for comprehensive observability.

Answer

Cloud Composer

Answer

AI Platform Pipelines

Answer

Cloud Logging

Question 11

A company has a batch prediction job that runs daily using AI Platform Batch Prediction. The job uses a TensorFlow model and processes 10 GB of data. Recently, the job started failing with the error 'The replica worker 0 exited with a non-zero exit code: Out of memory'. Which action should the team take to resolve this without rewriting the model?

Accepted Answer

Use a machine type with more memory, such as n1-highmem-8.. The error 'Out of memory' on replica worker 0 indicates that the machine type assigned to the prediction job does not have enough RAM to load the model and process the 10 GB batch. Increasing the machine type to one with more memory (e.g., n1-highmem-8) directly addresses the memory constraint without requiring any code changes. This is the most straightforward fix because AI Platform Batch Prediction allows you to specify machine types in the job configuration, and the error is purely a resource allocation issue.

Answer

Increase the number of workers (parallelism) to distribute the data across more machines.

Answer

Reduce the batch size parameter in the prediction job configuration.

Answer

Optimize the model to use less memory by pruning or quantization.

Question 12

A data science team needs to ensure that a deployed Vertex AI model can handle varying traffic patterns with minimal latency and cost. What should they do?

Accepted Answer

Use Vertex AI Prediction with autoscaling. Vertex AI Prediction with autoscaling dynamically adjusts the number of serving instances based on incoming traffic, ensuring minimal latency during spikes and cost efficiency during lulls. This is the recommended approach for handling variable traffic patterns in production, as it leverages Google Cloud's managed infrastructure to scale from zero to thousands of nodes automatically.

Answer

Use batch prediction instead of online

Answer

Pre-warm all instances

Answer

Deploy to a single large machine type

Question 13

A team trained a model on a Vertex AI custom training job and wants to deploy it to an endpoint for online predictions. They have the model artifacts stored in Cloud Storage. What steps are required?

Accepted Answer

Upload model to Model Registry, create endpoint, deploy model. To deploy a model for online predictions on Vertex AI, you must first upload the model artifacts from Cloud Storage to the Model Registry, which creates a versioned model resource. Then you create an endpoint (or use an existing one) and deploy the model to that endpoint, specifying machine type, traffic split, and other settings. This three-step process (upload → create endpoint → deploy) is the required workflow for online serving.

Answer

Directly deploy from Cloud Storage without Model Registry

Answer

Create endpoint, then upload model

Answer

Use Vertex AI Batch Prediction only

Question 14

A company uses Vertex AI Pipelines to orchestrate ML workflows. They want to automatically retrain the model when new data arrives, but only if the model's performance drops below a threshold. Which approach is best?

Accepted Answer

Use Vertex AI Model Monitor to detect skew and trigger retraining. Option C is correct because Vertex AI Model Monitoring can detect skew and drift, and can trigger retraining workflows automatically. Option A is wrong because scheduled triggers do not consider performance metrics. Option B is wrong because Cloud Functions would require custom logic to evaluate performance, which is more complex than using built-in monitoring. Option D is wrong because BigQuery scheduled queries are not integrated with model performance monitoring.

Answer

Use BigQuery scheduled queries to trigger pipeline

Answer

Trigger a pipeline on a schedule

Answer

Use Cloud Functions to evaluate performance and trigger pipeline

Question 15

A team deployed a model to Vertex AI Endpoint and notices latency spikes during peak hours. What should they first investigate?

Accepted Answer

Check if autoscaling is enabled and configured correctly. Latency spikes during peak hours typically indicate that the serving infrastructure is unable to handle the increased request volume. The first step is to check if autoscaling is enabled and configured correctly on the Vertex AI Endpoint, as this determines whether additional compute nodes are automatically provisioned to match demand. Without proper autoscaling, the endpoint will be overwhelmed, leading to queuing delays and latency spikes.

Answer

Switch to batch prediction

Answer

Reduce number of features

Answer

Increase machine type

PDE Operationalizing machine learning models Practice Test 6 — 15 Questions

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PDE Operationalizing machine learning models Practice Test 6 — 15 Questions

Exhibit