PMLE Timed Practice Test 2 — 10 Questions

Question 1

A financial services company has deployed a classification model on Vertex AI to detect fraudulent transactions. The model is monitored using Vertex AI Model Monitoring for skew and drift detection, and also logs predictions to BigQuery for analysis. After a month, the monitoring alerts show a significant drift in one feature (transaction_amount). Which TWO actions should the team take to diagnose and address this issue?

Accepted Answer

Compare the feature distribution in the training data with the recent serving data using statistical tests.. Option A is correct because comparing the feature distribution of the training data with recent serving data using statistical tests (e.g., Kolmogorov-Smirnov or Jensen-Shannon divergence) is the standard first step to quantify the drift and confirm it is statistically significant. This diagnostic action helps the team understand the nature and magnitude of the drift before deciding on remediation steps. Vertex AI Model Monitoring already performs such comparisons, but the team should independently verify the results in BigQuery to ensure accuracy.

Answer

Increase the frequency of model monitoring checks to every hour.

Answer

Increase the sampling rate for prediction logging to ensure full data capture.

Answer

Reduce the alert threshold to minimize false positives.

Question 2

Drag and drop the steps to set up model monitoring for drift detection on Vertex AI in the correct order.

Question 3

Drag and drop the steps to create and deploy a custom ML model on Vertex AI using a container in the correct order.

Question 4

Drag and drop the steps to set up a BigQuery ML linear regression model for forecasting in the correct order.

Question 5

Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.

Question 6

Drag and drop the steps to set up a distributed training job on Vertex AI using a custom container in the correct order.

Question 7

Drag and drop the steps to set up a batch prediction job using Vertex AI in the correct order.

Question 8

You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)

Accepted Answer

Use Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.. Vertex AI Pipelines (option B) is correct because it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs). This directly supports the requirement for automated, minimal-intervention weekly retraining by allowing you to define reusable components and schedule pipeline runs via Cloud Scheduler or event triggers, integrating natively with TensorFlow and Google Cloud services.

Answer

Use BigQuery scheduled queries to run the training script on a schedule.

Answer

Use AI Platform Notebooks to schedule the training job on a recurring basis.

Question 9

A manufacturing company wants to predict equipment failure using sensor data stored in BigQuery. They have limited ML expertise and want to use AutoML Tables. The data includes timestamps, numerical sensor readings, and a boolean 'failure' column. The dataset is highly imbalanced with only 1% failure cases. Which of the following is the most effective approach to handle the imbalance in AutoML Tables?

Accepted Answer

Let AutoML Tables handle the imbalance automatically; it has built-in techniques for class imbalance.. AutoML Tables has built-in techniques to handle class imbalance, such as automatically adjusting class weights and using stratified sampling during training. This allows the model to learn from the minority class without requiring manual data preprocessing, making it the most effective and simplest approach for users with limited ML expertise.

Answer

Downsample the majority class to balance the dataset.

Answer

Use a custom loss function in the training configuration.

Answer

Oversample the minority class using SQL before training.

Question 10

A team uses Vertex AI Feature Store to serve features for real-time predictions. They notice that feature values are frequently updated from multiple source systems, leading to inconsistencies. They need to ensure that feature values are consistent across all serving endpoints. What should they do?

Accepted Answer

Use a streaming ingestion pipeline with exactly-once semantics. Option D is correct because streaming ingestion with exactly-once semantics ensures that each feature update is applied precisely once, preventing duplicates or missed updates that cause inconsistencies. This approach synchronizes feature values across all serving endpoints in near real-time, directly addressing the problem of frequent updates from multiple source systems.

Answer

Use batch ingestion with weekly updates to reduce update frequency

Answer

Increase the offline storage TTL to retain historical feature values

Answer

Implement a manual approval process for feature updates