PMLE Practice Test 17 — 15 Questions

Question 1

A retail company uses Vertex AI Tabular (AutoML Tables) to build a customer churn prediction model. The training dataset contains 50,000 rows and 30 features, with a 5% churn rate. The model achieves an AUC of 0.85 on the test set. When deployed for online predictions, the average latency is 800ms, while the business requirement is under 200ms. The engineer has already reduced the feature set to 10 features, but latency only dropped to 600ms. The model size is 2GB. The endpoint is in us-central1 using an n1-standard-4 machine with minReplicaCount=1. What should the engineer do to meet the latency requirement?

Accepted Answer

Enable model compression in Vertex AI Tabular.. Vertex AI Tabular (AutoML Tables) supports model compression, which reduces model size and inference latency by applying techniques like quantization and pruning. Since the model is 2GB and latency is 600ms (still above the 200ms target), enabling compression can shrink the model significantly, often cutting latency by 2-3x, directly meeting the requirement without changing infrastructure or converting to a different framework.

Answer

Move the endpoint to a region geographically closer to the majority of customers.

Answer

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

Answer

Convert the model to a custom TensorFlow Lite model and deploy it.

Question 2

A retail company has been using Vertex AI AutoML to predict store-level demand for each product. They have a pipeline that runs nightly: data is extracted from BigQuery, preprocessed via Dataflow, and then used to train a new AutoML model each night. The model is deployed to a Vertex AI Endpoint for real-time inference. After two months, they notice that predictions for a new product category (recently launched) are consistently inaccurate, with predicted sales far exceeding actuals. They suspect data drift due to the new category. The data scientist has limited coding skills and wants a low-code solution. Which course of action should they take to improve predictions for the new category?

Accepted Answer

Add the product category as a feature in the AutoML dataset and retrain the model with the updated dataset. Adding the product category as a feature in the AutoML dataset allows the model to learn the distinct demand patterns of the new category directly from the data. Vertex AI AutoML automatically handles feature engineering and can adjust its predictions based on this categorical input, addressing the data drift without requiring custom code. This low-code approach leverages AutoML's built-in ability to incorporate new features and retrain with minimal manual intervention.

Answer

Retrain the model using only data from the new product category to specialize the model for that category

Answer

Use Vertex AI custom training with a Python script to fine-tune the model on the new category data

Answer

Remove the new product category from the training data because it causes bias, and rely on the pre-trained model's general pattern

Question 3

A financial services firm uses Vertex AI AutoML Natural Language to classify customer feedback into categories (positive, neutral, negative). They notice that the model performs poorly on neutral and negative classes, with high false negatives for negative. The dataset has 10,000 samples: 8,000 positive, 1,000 neutral, 1,000 negative. They have trained the model with automatic data split and default hyperparameters. Which course of action should they take to improve classification of minority classes?

Accepted Answer

Enable the 'weighted' option in AutoML NLP to handle class imbalance.. Option B is correct because AutoML Natural Language provides a built-in 'weighted' option that automatically adjusts the loss function to penalize misclassifications of minority classes more heavily, directly addressing the class imbalance without requiring custom model development. This is the simplest and most effective way to improve recall for the neutral and negative classes within the AutoML framework.

Answer

Use a custom model with a weighted loss function.

Answer

Increase the number of training node hours.

Answer

Set the data split to 50/25/25 for train/validation/test.

Question 4

Match each model evaluation metric to its use case.

Question 5

Match each ML acronym to its definition.

Question 6

Match each regularization technique to its effect.

Question 7

Match each Google Cloud AI/ML service to its primary purpose.

Question 8

Match each optimization algorithm to its characteristic.

Question 9

Match each MLOps practice to its description.

Question 10

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Question 11

Drag and drop the steps to set up a feature store for ML features using Vertex AI Feature Store in the correct order.

Question 12

Drag and drop the steps to perform a hyperparameter tuning job on Vertex AI in the correct order.

Question 13

Drag and drop the steps to implement a CI/CD pipeline for ML models using Cloud Build and Vertex AI in the correct order.

Question 14

A data scientist deployed a model to Vertex AI Prediction. When making a prediction request as shown in the exhibit, they receive a 400 error. What is the most likely cause?

Accepted Answer

The model was trained on 2 features, but the request provides 3 features.. The 400 error indicates a malformed request, typically due to a mismatch between the input features the model expects and what is provided. Since the model was trained on 2 features but the request includes 3 features, Vertex AI rejects the prediction as invalid input shape mismatch. This is the most common cause of 400 errors in Vertex AI Prediction when the instance structure does not match the model's signature.

Answer

The request JSON is malformed due to a missing comma between instances.

Answer

The endpoint path is incorrect; it should include the model version.

Answer

The request is sending 3 separate instances but the model expects only 1.

Question 15

A team deploys a model on Vertex AI that uses a custom prediction routine (CPR) with a dependency on a native library. The container crashes with 'ImportError: libcudart.so.11.0: cannot open shared object file'. How should they resolve this?

Accepted Answer

Build a custom container image that includes the CUDA runtime library.. The error 'ImportError: libcudart.so.11.0: cannot open shared object file' indicates that the CUDA runtime library (version 11.0) is missing from the container environment. Since the custom prediction routine (CPR) depends on a native library that requires this CUDA runtime, the correct solution is to build a custom container image that includes the CUDA runtime library. This ensures the shared object is available at runtime, resolving the import error.

Answer

Submit the model for batch prediction to avoid the error.

Answer

Request a GPU machine type for the endpoint.

Answer

Use a Vertex AI pre-built container for PyTorch instead.