PMLE Multiple Select — 40 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.

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Reduce the alert threshold to minimize false positives.

Question 2

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 3

Which TWO actions are appropriate when you detect that a production model's prediction distribution has shifted significantly from the training distribution?

Accepted Answer

Retrain the model using the most recent data. Option C is correct because retraining the model on the most recent data directly addresses the distribution shift by adapting the model to the new data patterns. This is a standard practice in MLOps when the shift is confirmed and the cause is understood, ensuring the model remains accurate and reliable in production.

Answer

Immediately roll back to the previous model version

Answer

Increase logging for future predictions

Answer

Reduce the traffic to the model to minimize impact

Question 4

A healthcare company uses AutoML Tables to predict patient readmission risk. The dataset contains 500,000 rows and 200 features, including patient demographics, lab results, and medical history. The model accuracy is lower than expected. The engineer wants to improve performance using low-code techniques. Which THREE actions are most effective? (Choose THREE.)

Accepted Answer

Remove highly correlated features using AutoML Tables' built-in feature importance analysis.. Option B is correct because AutoML Tables provides built-in feature importance analysis that can identify and remove highly correlated features, which reduces noise and multicollinearity, often improving model performance without manual intervention. This is a low-code technique that leverages the platform's automated capabilities to streamline feature selection.

Answer

Increase the training time budget to the maximum allowed.

Answer

Use a custom model architecture via AutoML Tables advanced options.

Question 5

A team has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?

Accepted Answer

Jensen-Shannon divergence (JSD). Jensen-Shannon divergence (JSD) is a symmetric, bounded (0 to 1) measure of the difference between two probability distributions, making it ideal for detecting drift in numerical features by comparing the training distribution to the serving distribution. It is a smoothed and normalized version of Kullback-Leibler divergence, and Vertex AI Prediction's Model Monitoring natively supports JSD for numerical feature drift detection.

Answer

Pearson correlation coefficient

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Chi-squared statistic

Answer

Population Stability Index (PSI)

Question 6

Which TWO actions are best practices when scaling a prototype ML model to production in Google Cloud?

Accepted Answer

Store and manage features in a feature store like Vertex AI Feature Store.. Vertex AI Feature Store centralizes feature management, ensuring consistency between training and serving. This eliminates training-serving skew by providing a single source of truth for features, which is critical when scaling from prototype to production.

Answer

Test the model only on a small sample of the production data to save costs.

Answer

Manually scale inference instances based on historical traffic patterns.

Answer

Use one-hot encoding for all categorical features without considering cardinality.

Question 7

Which TWO factors should you consider when choosing between BigQuery and Cloud Storage for storing training data? (Choose 2)

Accepted Answer

The format of the data: structured vs. unstructured.. Option A is correct because BigQuery is optimized for structured, tabular data (e.g., CSV, Avro, Parquet) and supports SQL queries, while Cloud Storage is a better fit for unstructured data (e.g., images, videos, raw text files) that does not require schema enforcement. Choosing the right storage depends on whether the training data has a fixed schema and requires relational querying or is blob-based and needs high-throughput access.

Answer

The requirement for data encryption at rest.

Answer

The need for fine-grained access control at the row level.

Answer

The maximum size of the dataset (BigQuery limit 1 TB).

Question 8

A company wants to build a low-code ML pipeline using Vertex AI Pipelines and BigQuery ML. They need to train, evaluate, and deploy a model. Which TWO statements are correct about the integration between Vertex AI Pipelines and BigQuery ML? (Choose TWO.)

Accepted Answer

Vertex AI Pipeline steps can include BigQuery ML training via the BigQueryQueryJob operator.. Option D is correct because Vertex AI Pipelines can integrate with BigQuery ML by using the BigQueryQueryJob operator to execute SQL-based training queries, such as `CREATE MODEL`, as a pipeline step. This allows you to orchestrate BigQuery ML model training within a Vertex AI Pipeline, enabling a low-code ML workflow.

Answer

BigQuery ML models are automatically stored in Vertex AI Model Registry after training.

Answer

BigQuery ML supports hyperparameter tuning using the CREATE MODEL statement.

Answer

Vertex AI Pipelines supports automatic retry of failed steps due to transient errors.

Question 9

A company trains a model using Vertex AI Training and then deploys it to Vertex AI Prediction. They notice that prediction requests fail with 'InvalidArgument: input tensor shape mismatch'. Which THREE are possible causes?

Accepted Answer

The input data types do not match the expected types (e.g., float vs int). Option C is correct because Vertex AI Prediction expects the input tensor data types to exactly match those used during model training. If the model was trained with float32 inputs but the prediction request sends int32 values, the serving infrastructure detects the mismatch and returns an 'InvalidArgument: input tensor shape mismatch' error, as TensorFlow Serving (which underlies Vertex AI Prediction) validates dtype consistency at the graph level.

Answer

The model was exported in a different format than supported

Answer

The batch size in the request is too large

Question 10

A company wants to reduce costs for serving a model on Vertex AI Prediction without sacrificing availability. Which THREE strategies should they consider?

Accepted Answer

Enable automatic batching to improve throughput per instance. Option C is correct because enabling automatic batching on Vertex AI Prediction allows the model server to group multiple inference requests into a single batch, which increases throughput per instance and reduces the total number of compute resources needed. This directly lowers serving costs without sacrificing availability, as the batching is handled transparently by the Vertex AI Prediction infrastructure.

Answer

Use larger machine types to reduce the number of replicas

Answer

Switch to HTTP/2 to reduce network overhead

Question 11

An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)

Accepted Answer

Set up an A/B experiment comparing the model's recommendations against a baseline. Option B is correct because an A/B experiment directly measures the model's real-world impact by comparing its CTR against a baseline (e.g., random or popularity-based recommendations). This isolates the model's performance from confounding factors like seasonality or user behavior changes, providing a causal estimate of its effectiveness.

Answer

Run offline evaluation on a holdout dataset to confirm accuracy

Answer

Increase the training dataset size by including data from two years ago

Question 12

Which TWO metrics should you monitor to detect data drift in a batch prediction pipeline?

Accepted Answer

Feature distribution drift (e.g., KS test). Feature distribution drift (C) is correct because it directly measures changes in the input data distribution over time using statistical tests like the Kolmogorov-Smirnov (KS) test, which compares the cumulative distribution of a feature in the current batch against a reference baseline. This is a primary indicator of data drift, as shifts in feature distributions can degrade model performance even if labels are not immediately available.

Answer

Model accuracy on recent labeled data

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Model prediction latency

Answer

Training data size

Question 13

Which THREE components should you include in a comprehensive model monitoring dashboard for a production ML system?

Accepted Answer

System resource utilization (CPU, memory, latency). Option B is correct because system resource utilization metrics (CPU, memory, latency) are essential for monitoring the health and performance of the production infrastructure hosting the ML model. These metrics help detect resource bottlenecks, scaling issues, or degradation that could impact inference latency and throughput, which are critical for maintaining service-level objectives (SLOs).

Answer

Team member roles and responsibilities

Answer

Training pipeline code version

Question 14

A team has trained a sentiment analysis model using PyTorch on Vertex AI Training. They now want to deploy it for online predictions with low latency. Which TWO actions should they take? (Choose 2)

Accepted Answer

Use a machine type with a GPU for faster inference.. Option B is correct because GPU-accelerated inference significantly reduces latency for deep learning models like sentiment analysis, especially when using PyTorch, which has native CUDA support. Vertex AI Prediction supports GPU machine types (e.g., n1-standard-4 with NVIDIA T4) that can process batched requests faster than CPUs, directly addressing the low-latency requirement.

Answer

Create multiple model versions for A/B testing.

Answer

Enable batch prediction instead of online prediction.

Answer

Convert the model to TensorFlow SavedModel format.

Question 15

Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

Accepted Answer

Feature Store provides a centralized repository for feature data.. Option D is correct because Vertex AI Feature Store is designed as a centralized repository that organizes, stores, and serves feature data consistently across different models and pipelines. This centralization ensures feature reuse, consistency, and governance, preventing data silos and duplication across the ML lifecycle.

Answer

Feature Store automatically applies feature engineering transformations.

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Feature Store can only store numerical features.

Answer

Feature Store can only be used with Vertex AI models.

Question 16

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

Accepted Answer

Monitor model performance and data drift continuously.. Option B is correct because continuous monitoring of model performance and data drift is essential for maintaining prediction accuracy in production. Google Cloud's Vertex AI Model Monitoring automatically detects skew and drift by comparing serving data against training data distributions, alerting you to degradation before it impacts business outcomes.

Answer

Manually tune hyperparameters for each retraining run.

Answer

Store all raw training data indefinitely for auditability.

Question 17

A company is deploying a model for online predictions on Vertex AI. They want to minimize latency while also handling traffic spikes. Which TWO configurations should they choose?

Accepted Answer

Use GPU machine type. Option A is correct because GPU machine types on Vertex AI provide significantly faster inference for deep learning models, reducing latency per prediction. Option B is correct because enabling autoscaling with min replicas=1 ensures the model can handle traffic spikes by dynamically adding replicas while keeping at least one instance running to avoid cold starts.

Answer

Disable autoscaling and use manual scaling

Answer

Use CPU machine type with more memory

Answer

Set a fixed number of replicas equal to peak load

Question 18

A company has a prototype ML model that predicts equipment failure. They want to deploy it to production using Vertex AI. The model must be retrained weekly with new data. They also need to monitor for data drift and model performance. Which THREE components should they include in their MLOps pipeline? (Choose 3)

Accepted Answer

A scheduled training pipeline that retrains the model weekly.. Option A is correct because the requirement specifies weekly retraining, which is best implemented as a scheduled training pipeline in Vertex AI using Cloud Scheduler or a recurring AI Platform Pipeline run. This automates the retraining process without manual intervention, ensuring the model stays current with new data.

Answer

A manual QA step where data scientists approve each deployment.

Answer

A manual review of new data before it is used for training.

Question 19

Which TWO are best practices for implementing a low-code ML solution using Vertex AI AutoML? (Choose 2)

Accepted Answer

Use the AutoML recommended data split (train/validation/test) to avoid overfitting.. Option A is correct because AutoML's recommended data split (train/validation/test) is designed to prevent overfitting by ensuring the model is evaluated on unseen data. AutoML automatically handles the split ratio (e.g., 80/10/10) and stratification, which is a best practice for low-code ML solutions where manual split logic is error-prone.

Answer

Impute missing values manually before uploading the dataset.

Answer

Normalize numerical features to zero mean and unit variance.

Answer

Export the data and train a custom model with a different architecture.

Question 20

A machine learning team is deploying a model for real-time predictions using Vertex AI. They need to ensure that the deployment follows best practices for collaboration and governance. Which TWO actions should they take?

Accepted Answer

Use a continuous integration/continuous deployment (CI/CD) pipeline to deploy model versions.. Option A is correct because using a CI/CD pipeline for deploying model versions ensures automated, repeatable, and auditable deployments, which is a best practice for collaboration and governance. This approach enforces version control, testing, and approval gates, reducing the risk of errors and enabling rollback if needed.

Answer

Store all model artifacts in a local file system to reduce latency.

Answer

Manually configure autoscaling parameters for the endpoint.

Answer

Allow any team member to deploy directly to production without review.