PMLE Practice Test 13 — 15 Questions

Question 1

A team uses custom training and deploys a TensorFlow model using Vertex AI Endpoints. They set up Cloud Monitoring alerts for online prediction latency. However, they notice the latency metric shows a spike every hour, but the actual user experience is fine. What could be the cause?

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

Accepted Answer

The metric includes prediction time plus log writing time. Vertex AI Endpoints' latency metric includes both the model inference time and the time taken to write prediction logs to Cloud Logging. This log writing occurs asynchronously but can cause periodic spikes in the reported latency metric when log buffers flush, even though the actual user-facing prediction latency remains unaffected. The spike every hour aligns with log rotation or buffer flush intervals, not with actual prediction performance degradation.

Answer

The alert threshold is too low

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The metric is being sampled every hour

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A monitoring agent on the VM is causing additional latency

Question 2

Which TWO options are recommended practices for managing model versions across teams in Google Cloud?

Accepted Answer

Use Vertex AI Model Registry to track model versions and their deployment history. Vertex AI Model Registry is the recommended service for managing model versions across teams because it provides a centralized repository to track model versions, their associated metadata, and deployment history. It integrates natively with Vertex AI endpoints and pipelines, enabling consistent governance and lineage tracking across the ML lifecycle.

Answer

Store all model files in a GitHub repository

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Maintain a custom database to map model names to artifact locations

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Use AI Platform (Unified) Models as the primary model registry

Question 3

An organization wants to implement continuous training for a model that serves predictions via Vertex AI Endpoints. Which approach best automates the retrain-deploy cycle?

Accepted Answer

Schedule a Vertex AI Pipeline to retrain and conditionally deploy. Vertex AI Pipelines can be scheduled to run a retraining workflow and include a conditional step that deploys the new model to the endpoint only if it passes validation (e.g., evaluation metrics meet a threshold). This fully automates the retrain-deploy cycle without manual intervention, leveraging the pipeline's orchestration capabilities.

Answer

Use Vertex AI Model Registry to auto-deploy on new model upload

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Manually retrain and deploy monthly

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Use Cloud Composer to schedule retraining only

Answer

Use a Cloud Function to retrain the model and update the endpoint

Question 4

You want to deploy a TensorFlow model to a Vertex AI endpoint and enable online predictions. The model requires GPU for inference. Which machine type should you select when deploying the model?

Accepted Answer

a2-highgpu-1g (with A100 GPU). The a2-highgpu-1g machine type is specifically designed for GPU-accelerated workloads on Vertex AI, featuring an NVIDIA A100 GPU that meets the inference requirements of a TensorFlow model. Vertex AI online prediction endpoints require a machine type that supports GPU attachment, and the A2 series is the only option among the choices that provides a dedicated GPU for inference.

Answer

n1-standard-4

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e2-standard-4

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n1-highmem-8

Question 5

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 6

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.. 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 7

An ML engineer is trying to upload a TensorFlow model to Vertex AI using the gcloud command shown. The model was trained using TensorFlow 2.11 and saved with model.save('model/'). The engineer sees the error. What is the most likely cause?

Accepted Answer

The model was saved in a format other than SavedModel (e.g., HDF5) or the artifact path does not contain the expected directory structure.. The error indicates that Vertex AI cannot find the expected SavedModel artifacts (saved_model.pb and variables/ directory) at the specified path. When using model.save('model/') with TensorFlow 2.11, the default format is the SavedModel format, but the artifact path must point to the directory containing the saved_model.pb file, not a parent directory or a model saved in HDF5 format. The gcloud command likely references a path that does not contain the required SavedModel structure, causing the upload to fail.

Answer

The container port should be 8080 instead of 8501.

Answer

The service account does not have permission to access the bucket.

Answer

The container image is for TensorFlow 2.11 but the model was saved with an older version.

Question 8

You have a Vertex AI endpoint serving a model for real-time predictions. The endpoint is configured with minReplicaCount=2 and maxReplicaCount=10. Over the past week, you notice that the actual number of replicas rarely exceeds 2, but the average CPU utilization is around 85%. You want to reduce costs without impacting performance. What should you do?

Accepted Answer

Decrease minReplicaCount to 1.. Decreasing minReplicaCount to 1 allows the endpoint to scale down to a single replica when traffic is low, reducing compute costs. Since the actual replica count rarely exceeds 2, the current minReplicaCount=2 forces at least two replicas to run continuously, even when one would suffice. With average CPU utilization at 85%, the model is already efficiently handling load, so scaling down to one replica will not impact performance while saving costs.

Answer

Increase minReplicaCount to 5.

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Increase maxReplicaCount to 20.

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Decrease the CPU utilization target to 50%

Question 9

A team is troubleshooting a Vertex AI Pipelines run that keeps failing at the model evaluation step. The pipeline includes steps: data preprocessing, training, evaluation, and deployment. Which THREE actions should they take to diagnose the issue?

Accepted Answer

Verify that the training step output is correctly linked as input to evaluation.. Vertex AI Pipelines relies on precise input/output artifact linking between steps. If the training step's output (e.g., a model artifact or evaluation metrics) is not correctly wired as the input to the evaluation step, the pipeline will fail due to missing or mismatched data. This is a common misconfiguration in Kubeflow Pipelines DSL, where step outputs must be explicitly passed as arguments to downstream components.

Answer

Increase the memory of the evaluation step's machine.

Answer

Replace the evaluation step with a Vertex AI Model Evaluation service.

Question 10

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.. 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.

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Store all raw training data indefinitely for auditability.

Question 11

You are using Vertex AI Matching Engine for similarity search. Your index has 10 million embeddings of 512 dimensions. The query latency requirement is under 10ms for 99th percentile. Which index type should you choose?

Accepted Answer

Approximate Nearest Neighbor (ANN) index using the ScaNN algorithm.. The ScaNN (Scalable Nearest Neighbors) algorithm is specifically designed for high-dimensional, large-scale similarity search with strict latency requirements. With 10 million 512-dimensional embeddings, an ANN index like ScaNN can achieve sub-10ms query latency at the 99th percentile by trading a small amount of recall for dramatic speed improvements, which is exactly what Vertex AI Matching Engine optimizes for.

Answer

Brute-force index with cosine distance.

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A custom distance-based index using Cloud SQL.

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A tree-based index from scikit-learn deployed as a custom container.

Question 12

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. 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

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Use CPU machine type with more memory

Answer

Set a fixed number of replicas equal to peak load

Question 13

A company uses Vertex AI Vector Search for similarity search. They have a dataset of 10 million 512-dimensional vectors. Which index type should they choose for lowest latency at high recall?

Accepted Answer

Approximate nearest neighbor (ANN) index with Scann. For a dataset of 10 million 512-dimensional vectors, a brute-force (flat) index would be far too slow for low-latency queries. Approximate Nearest Neighbor (ANN) with ScaNN (Scalable Nearest Neighbors) is specifically designed by Google for high-dimensional vector search, offering sub-linear query time while maintaining high recall through techniques like anisotropic quantization and tree-based partitioning. This makes it the optimal choice for balancing latency and recall at this scale.

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Brute-force (flat) index

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Tree-based index

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Hashing-based index

Question 14

A company wants to serve a large XGBoost model that exceeds the 2GB limit for Vertex AI Prediction. What should they do?

Accepted Answer

Use a custom container to serve the model. Vertex AI Prediction has a 2GB limit for the model artifact when using pre-built containers. A custom container bypasses this limit because you package the model and serving code into a Docker image, which can be arbitrarily large. This allows you to serve XGBoost models exceeding 2GB without size constraints imposed by the managed serving infrastructure.

Answer

Reduce model size by removing features

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Compress the model using gzip and upload

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Deploy the model on Cloud Run Functions

Question 15

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