PMLE Practice Test 2 — 10 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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.

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Brute-force index

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Hash-based index

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Tree-based index

Question 2

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.

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

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

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

Question 3

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.

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

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Switch to batch prediction to avoid online cold start.

Question 4

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.

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

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The trained model is stored in Cloud Storage

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The model must be exported to Vertex AI for prediction

Question 5

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.

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

Question 6

An ML team is moving from a prototype Jupyter notebook to a production training pipeline. They want to ensure reproducibility. Which approach should they take?

Accepted Answer

Use a container with fixed dependencies and record hyperparameters.. Using a container with fixed dependencies and recording hyperparameters ensures that the training environment and configuration are captured, enabling exact reproduction. Option A is wrong because interactive parameter tuning is not reproducible—it introduces manual adjustments. Option C is wrong because exporting the notebook's output model directly lacks environment tracking and hyperparameter records. Option D is wrong because saving the notebook as a .py file does not capture the full environment or dependencies.

Answer

Use interactive parameter tuning.

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Export the notebook's output model directly.

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Save the notebook as a .py file.

Question 7

Your PyTorch training script uses DistributedDataParallel (DDP) across 4 vertices each with 4 GPUs (16 GPUs total). You submit a Vertex AI custom training job. How should you configure the worker pool spec?

Accepted Answer

Create one worker pool with 4 replicas, each with machine type having 4 GPUs. For DDP across multiple machines, use MultiWorkerMirroredStrategy equivalent in PyTorch: set replicas to 4, each with machine type having 4 GPUs. The TF_CONFIG env var is not needed; Vertex AI sets necessary environment variables for distributed training.

Answer

Create a chief worker pool with 1 replica (4 GPUs) and a parameter server pool with 4 replicas (no GPUs)

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Create 4 separate jobs, each with 1 replica and 4 GPUs

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Create one worker pool with 16 replicas, each with 1 GPU

Question 8

A machine learning team is deploying a PyTorch model on Vertex AI Prediction for real-time inference. The model was trained with preprocessing that includes tokenization and normalization. They want to embed the preprocessing logic in the model to reduce prediction latency and avoid additional service calls. Which approach should they take?

Accepted Answer

Use TorchScript to trace the preprocessing steps and export the entire pipeline as a single scripted model. TorchScript allows you to trace or script a PyTorch model, including preprocessing operations like tokenization and normalization, into a single serialized artifact. By embedding preprocessing in the TorchScript model, the entire pipeline runs in one forward pass on the Vertex AI endpoint, eliminating extra service calls and reducing latency. This is the standard approach for consolidating preprocessing with a PyTorch model for real-time inference.

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Deploy the preprocessing logic as a Cloud Function and invoke it before calling the prediction endpoint

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Wrap the preprocessing logic in a Flask application and deploy it as a separate microservice in front of the prediction endpoint

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Use TensorFlow Transform to convert preprocessing into a SavedModel and call it from the PyTorch model

Question 9

A data scientist trains an XGBoost model on Vertex AI with a custom container. The model performs well on a held-out test set but fails to generalize in production. They suspect data leakage between training and validation. What is the best practice to prevent this?

Accepted Answer

Store and serve features using Vertex AI Feature Store with point-in-time correctness. Vertex AI Feature Store with point-in-time correctness ensures that for each training example, only feature values that were known at the time of the prediction (i.e., before the label occurred) are used. This prevents future data from leaking into the training set, which is the most common cause of poor generalization when temporal ordering matters. The Feature Store automatically retrieves the latest feature value as of a specified timestamp, eliminating the need for manual joins and windowing logic.

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Implement feature engineering in Vertex AI Pipelines to ensure temporal ordering

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Store all features in BigQuery and join on timestamp during training and serving

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Use Vertex AI AutoML instead of custom training

Question 10

To enable collaboration on notebook-based experiments across teams, what is the recommended approach in Google Cloud?

Accepted Answer

Use Colab Enterprise notebooks with shared runtimes and IAM permissions. Colab Enterprise notebooks with shared runtimes and IAM permissions is the recommended approach because it provides a fully managed, collaborative environment where multiple users can work on the same notebook simultaneously, with fine-grained access control via IAM and consistent runtime configurations. This eliminates version conflicts and environment drift, which are common in distributed notebook workflows.

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Share Docker images containing the notebook environment

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Each team member works on their own local Jupyter notebook and shares screenshots

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Store notebooks in a Cloud Storage bucket and open them with Vertex AI Workbench