PMLE Practice Questions

Question 1

A data science team deploys a regression model to predict house prices. After one month, the mean absolute error (MAE) on the serving data increases by 20% compared to the test set. Which monitoring strategy should the team implement first to diagnose the issue?

Accepted Answer

Monitor prediction residuals and compute serving-time MAE over sliding windows.. The first step in diagnosing a 20% MAE increase on serving data is to monitor prediction residuals over sliding windows. This directly tracks how model errors evolve in production, allowing the team to detect whether performance degradation is sudden or gradual, and to correlate it with specific time windows or data slices. Computing serving-time MAE on sliding windows provides an immediate, interpretable signal of model health without assuming the root cause.

Answer

Retrain the model daily with the latest data to adapt to changing patterns.

Answer

Compare the distribution of training labels with serving labels using a two-sample t-test.

Answer

Monitor input feature distributions for drift using the Kolmogorov-Smirnov test.

Question 2

In a Vertex AI Pipeline, a component produces a Metrics artifact that includes an evaluation metric. The engineer wants to use this metric value as a condition to decide whether to deploy the model. However, the metric value is stored in the artifact's metadata and not directly as a pipeline parameter. How can the engineer pass the metric value to a downstream conditional task?

Accepted Answer

Add a component that reads the artifact's metadata and outputs the metric as a parameter, then use that parameter in the condition.. Vertex AI Pipeline conditions require pipeline parameters (typed values) to evaluate expressions like `dsl.If`. A Metrics artifact's metadata is stored as an artifact property, not a pipeline parameter, so a custom component must read that metadata and output the metric as a parameter. This parameter can then be used in the `dsl.If` condition to control downstream deployment.

Answer

Configure the component that produces the Metrics artifact to also output the metric as a pipeline parameter.

Answer

Use the importer component to convert the artifact into a parameter.

Answer

Use the artifact directly in the dsl.If condition, as artifacts are comparable.

Question 3

Match each model evaluation metric to its use case.

Accepted Answer

Accuracy: Correct when classes are balanced and errors have equal cost. Accuracy is correctly matched with balanced classes and equal error cost; Precision with minimizing false positives; Recall with minimizing false negatives; F1 Score with balancing precision and recall for imbalanced classes. Option E incorrectly pairs Accuracy with 'when false positives are costly' which is actually the domain of Precision. The main trap is confusing accuracy with precision when dealing with asymmetric costs.

Answer

Accuracy: When false positives are costly

Question 4

You are fine-tuning a large language model (LLM) from Vertex AI Model Garden using a custom dataset. You need to minimize training cost while maintaining reasonable throughput. Which THREE strategies should you combine?

Accepted Answer

Use spot VM instances for training. Spot VM instances are significantly cheaper than on-demand instances, reducing training cost. They can be preempted, but for fine-tuning tasks that can checkpoint and resume, this trade-off is acceptable for cost savings.

Answer

Use full fine-tuning of all model parameters

Answer

Use TPU v4 pods for training

Question 5

A team is using Vertex AI AutoML to train a forecasting model. They need to retrain the model weekly and only if the new week's data significantly changes the data distribution. What is the most efficient way to achieve this?

Accepted Answer

Use Vertex AI Model Monitoring to detect drift and trigger a pipeline. Vertex AI Model Monitoring can be configured to detect data drift on the model's input features, and when drift exceeds a threshold, it can trigger a Cloud Function or a Vertex AI pipeline to retrain the model. This approach avoids unnecessary retraining when the data distribution has not changed significantly, which is more efficient than always retraining. The integration with Cloud Functions or Pub/Sub allows for a serverless, event-driven retraining pipeline that only runs when needed.

Answer

Use a scheduled pipeline that always retrains

Answer

Use Cloud Monitoring alerts on data drift to trigger retraining

Answer

Use Cloud Functions on schedule to compare distributions

Question 6

A company is training a large neural network on Vertex AI and training jobs keep failing with 'Out of memory' errors. The VM uses a standard n1-standard-4 machine with 15 GB RAM. Which action should they take first?

Accepted Answer

Reduce the batch size in the training script. The 'Out of memory' error on a n1-standard-4 VM (15 GB RAM) indicates the model's memory footprint exceeds available RAM. Reducing the batch size directly decreases the memory required for storing intermediate activations and gradients during training, which is the most immediate and cost-effective fix without changing the underlying infrastructure.

Answer

Use a larger machine type like n1-standard-16

Answer

Enable distributed training across multiple VMs

Answer

Switch the training to CPU only

Question 7

An ML team wants to automatically retrain a model when data drift is detected. They have set up a Cloud Monitoring alert on drift. What service should they use to trigger a retraining pipeline in response to the alert?

Accepted Answer

Cloud Functions. Cloud Functions is the correct service to trigger a retraining pipeline in response to a Cloud Monitoring alert. Cloud Monitoring can publish alerts to a Pub/Sub topic, and a Cloud Function subscribed to that topic can invoke the Vertex AI pipeline or trigger a Cloud Scheduler job that starts retraining. This event-driven pattern is the standard way to automate retraining on drift detection.

Answer

Cloud Scheduler

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Monitoring

Question 8

An ML engineer notices that predictions are taking longer than expected under moderate traffic. Reviewing the endpoint configuration, what is the most likely cause of the high latency?

Accepted Answer

The accelerator count is 0, meaning no GPU is used.. When the accelerator count is set to 0, the endpoint runs inference on the CPU only, which is significantly slower than GPU-accelerated inference for deep learning models. This is the most direct cause of high latency under moderate traffic, as the model's compute demands exceed CPU throughput.

Answer

Container logging is disabled, slowing down request processing.

Answer

The machine type n1-standard-4 is underpowered for the model's compute needs.

Answer

Automatic scaling is set with a maxReplicaCount of 10, which creates overhead.

Question 9

You are using Vertex AI Vector Search for a product recommendation system. Your index is updated with new embeddings every hour. To minimize query latency while keeping the index fresh, what should you do?

Accepted Answer

Use streaming updates to insert new embeddings into the deployed index.. Vertex AI Vector Search supports streaming updates that let you insert, update, or delete datapoints in a deployed index without rebuilding it, so queries stay low-latency while the index remains fresh. This is the intended mechanism for near-real-time freshness with minimal disruption. Batch rebuilds and redeployments cause downtime and latency spikes, which the question explicitly asks to avoid.

Answer

Rebuild the entire index hourly as a batch job and redeploy it.

Answer

Create a new index each hour and use traffic splitting to gradually shift traffic.

Answer

Use a brute-force index instead of ANN to ensure accuracy after updates.

Question 10

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

Accepted Answer

1. Build and push the container, 2. Register the model, 3. Deploy to an endpoint, 4. Test. The correct order to create and deploy a custom ML model on Vertex AI using a container is: First, build and push the container image to Artifact Registry. Next, register the model in Vertex AI Model Registry. Then, deploy the model to an endpoint using Vertex AI Endpoints. Finally, test the endpoint by sending prediction requests to verify it works.

Answer

1. Register the model, 2. Build and push the container, 3. Deploy to an endpoint, 4. Test

Answer

1. Build and push the container, 2. Deploy to an endpoint, 3. Register the model, 4. Test

Answer

1. Build and push the container, 2. Register the model, 3. Test, 4. Deploy to an endpoint

Question 11

A company needs to perform real-time similarity search on a dataset of 10 million embedding vectors. They expect low latency (under 10ms) and high throughput. Which index type should they use in Vertex AI Vector Search?

Accepted Answer

Approximate nearest neighbor (ANN) index with ScaNN. For large datasets requiring low latency, an approximate nearest neighbor (ANN) index is appropriate. The Scann algorithm (ScaNN) is used by Vertex AI Vector Search for ANN.

Answer

Brute-force index

Answer

Hash-based index

Answer

Tree-based index

Question 12

A data science team uses TFX to train and deploy a model on Vertex AI. They want automated monitoring for pipeline health. Which set of metrics should they monitor to quickly detect issues in the training pipeline?

Accepted Answer

Pipeline execution status (success/failure), component completion times, and data validation anomalies.. The question specifically asks about monitoring the training pipeline's health, not the serving infrastructure. Pipeline execution status directly indicates whether the pipeline ran successfully, component completion times help identify bottlenecks or failures, and data validation anomalies catch data quality issues early in the pipeline — all of which are essential for detecting issues in the training pipeline itself.

Answer

Prediction request count, latency, and error rate on the serving endpoint.

Answer

Number of pipeline runs, average CPU utilization, and memory usage.

Answer

Model accuracy, precision, and recall on the evaluation dataset.

Question 13

You are using Vertex AI Prediction with a custom container that requires a large model file (5 GB). Deployment takes 10 minutes to start. You want to reduce cold start latency. Which action would be MOST effective?

Accepted Answer

Set minReplicas to 1 to keep at least one instance always running.. Setting minReplicas to 1 keeps at least one prediction instance warm, eliminating the cold start caused by loading the 5 GB model from scratch on each new deployment. Vertex AI Prediction scales replicas based on traffic; with minReplicas=1, the model stays loaded in memory and responds immediately. Compression, local SSD, and batch prediction do not address the fundamental issue of keeping a warm online endpoint.

Answer

Compress the model file and decompress on startup.

Answer

Use a machine type with local SSD to speed up model loading.

Answer

Switch to batch prediction to avoid online cold start.

Question 14

A data analyst wants to use BigQuery ML to train a linear regression model (LINEAR_REG) to predict house prices. They have a table with features like square footage, number of bedrooms, and location. Which TWO statements about the training process are correct?

Accepted Answer

The model is automatically evaluated on a held-out test set if data splitting is enabled. When data splitting is enabled in BigQuery ML, the `CREATE MODEL` statement automatically reserves a portion of the input data as a held-out test set. After training completes, BigQuery ML evaluates the model on this test set and reports metrics like mean absolute error and R², without requiring any manual split or separate evaluation step.

Answer

The analyst must call ML.TRAIN after CREATE MODEL to start training

Answer

The trained model is stored in Cloud Storage

Answer

The model must be exported to Vertex AI for prediction

Question 15

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 to retrain weekly with new data is met by a scheduled training pipeline in Vertex AI, typically implemented with Vertex AI Pipelines and a Cloud Scheduler or scheduled run that re-executes the training job on the latest data. Option D is correct because automated redeployment when performance drops below a threshold is a core MLOps practice; in Vertex AI this can be done by monitoring model performance and using a trigger (for example, a Cloud Function or Eventarc/Cloud Monitoring alert) to promote a newly trained model to an endpoint. Option E is correct because Vertex AI Model Monitoring detects data drift and can emit alerts via Cloud Monitoring, which is exactly what the scenario requires for drift and performance oversight. Option B is not correct because a manual QA approval step for every deployment is not required by the scenario and would slow down the automated weekly retraining and redeployment workflow. Option C is not correct because manual review of all new data before training is not a required MLOps component here and would prevent the pipeline from being fully automated and scheduled weekly.

Answer

A manual QA step where data scientists approve each deployment.

Answer

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

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