PMLE Serving and Scaling Models Practice Test 7 — 15 Questions

Question 1

A company uses Vertex AI Matching Engine for real-time recommendations. They need to serve queries with low latency and support frequent updates. Which two configurations are appropriate? (Choose 2)

Accepted Answer

Enable streaming updates for the index. Vertex AI Matching Engine supports streaming updates, which allow real-time insertion, deletion, and modification of vectors without rebuilding the entire index. This is essential for use cases requiring frequent updates, such as real-time recommendation systems, because it maintains low latency for serving queries while keeping the index current.

Answer

Store the index in Cloud Storage and query via Python

Answer

Use a brute-force index for exact results

Answer

Use batch updates only

Question 2

A company is deploying a new model version to an existing Vertex AI endpoint. They want to test the new version with 5% of traffic before fully rolling it out. What is the correct approach?

Accepted Answer

Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request.. Vertex AI endpoints support traffic splitting between multiple deployed models. By deploying the new model version to the same endpoint and setting `traffic_split` to 5% for the new version and 95% for the existing version, the endpoint automatically routes a corresponding proportion of inference requests to each model without any client-side changes.

Answer

Create a new endpoint for the new version and update the client to call both endpoints.

Answer

Deploy the new version and set the minimum replicas to 0, then gradually increase.

Answer

Use Cloud Load Balancing to distribute traffic between two endpoints.

Question 3

Which Vertex AI service is best suited for finding similar items in a large dataset based on embedding vectors, such as product recommendations or image similarity search?

Accepted Answer

Vertex AI Matching Engine. Vertex AI Matching Engine is specifically designed for high-performance vector similarity search (also known as approximate nearest neighbor search) using embedding vectors. It scales to billions of vectors and is ideal for use cases like product recommendations and image similarity search, where you need to find the most similar items based on dense vector representations.

Answer

Vertex AI Prediction Endpoint

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Feature Store

Question 4

An ML engineer needs to run batch predictions on 10 TB of data stored in BigQuery using a TensorFlow model. The predictions must be written to BigQuery. Which service should they use?

Accepted Answer

Use Vertex AI Batch Prediction with BigQuery source and sink.. Vertex AI Batch Prediction supports BigQuery as both source and sink, enabling direct batch prediction without additional infrastructure.

Answer

Create a Dataflow pipeline to read from BigQuery, run the model using Python, and write results to BigQuery.

Answer

Export BigQuery data to GCS, run batch prediction on GCS, then load results back to BigQuery.

Answer

Use Vertex AI online prediction with batch requests.

Question 5

A company uses Vertex AI for online predictions with a large ensemble model that requires GPU acceleration. They want to reduce inference latency by batching multiple requests into a single GPU inference call. What should they configure?

Accepted Answer

Deploy the model with NVIDIA Triton Inference Server configured for dynamic batching.. NVIDIA Triton Inference Server supports dynamic batching, which automatically groups multiple inference requests into a single GPU call. This reduces overhead and improves GPU utilization, directly addressing the need to lower latency for online predictions with a large ensemble model on Vertex AI.

Answer

Use Vertex AI Model Optimization for automatic compilation.

Answer

Increase the number of GPU replicas to handle higher concurrency.

Answer

Enable model quantization using TensorRT.

Question 6

A data scientist wants to deploy a model trained with PyTorch to a Vertex AI endpoint for online predictions. What is the recommended approach?

Accepted Answer

Package the model in a custom container with a web server (e.g., FastAPI) and deploy to Vertex AI.. Vertex AI requires a custom container for PyTorch models, as it does not provide a pre-built PyTorch serving container. The recommended approach is to package the trained PyTorch model with a web server like FastAPI (or Flask) that loads the model and exposes an HTTP endpoint for predictions. This container is then deployed to Vertex AI for online predictions, giving full control over the inference environment and dependencies.

Answer

Use Vertex AI's pre-built PyTorch container and upload the state dictionary.

Answer

Export the PyTorch model to a SavedModel format and deploy using Vertex AI's pre-built TensorFlow container.

Answer

Convert the model to TensorFlow.js and deploy to Cloud Functions.

Question 7

Which Vertex AI feature allows you to reduce the size of a trained model to improve inference speed on edge devices without significant accuracy loss?

Accepted Answer

Vertex AI Model Optimization. Vertex AI Model Optimization is the correct feature because it provides model quantization, pruning, and distillation techniques specifically designed to reduce model size and improve inference latency on edge devices. This service applies post-training quantization (e.g., FP32 to INT8) and structured weight pruning to shrink the model footprint while maintaining accuracy within acceptable thresholds, directly addressing the need for efficient deployment on resource-constrained hardware.

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Matching Engine

Answer

Vertex AI Continuous Training

Question 8

An application serving predictions from a Vertex AI endpoint receives many identical requests within a short time window. The team notices redundant computation and wants to cache responses to reduce latency and cost. What is the recommended solution?

Accepted Answer

Implement a cache layer using Cloud Memorystore for Redis, hashing prediction requests.. Vertex AI does not provide built-in request caching; instead, the recommended pattern is to implement an external cache like Cloud Memorystore for Redis. By hashing the prediction request payload and using it as a cache key, identical requests within the short time window can be served from Redis, eliminating redundant model inference and reducing both latency and cost.

Answer

Deploy the model on a larger machine type to handle duplicate requests faster.

Answer

Enable Vertex AI endpoint caching by setting the `enable_cache` flag.

Answer

Use Cloud CDN in front of the endpoint.

Question 9

A team is deploying a model that has strict latency requirements: p99 response time under 100 ms. The model is CPU-only and will receive up to 1000 QPS. They want to minimize cost while meeting the SLO. Which machine type and scaling configuration is most appropriate?

Accepted Answer

n1-standard-4 with min_replicas=1 and max_replicas=5, CPU utilization target 60%. It uses a CPU-only machine (n1-standard-4) with autoscaling based on CPU utilization target of 60%, which balances cost and performance for a latency-sensitive, CPU-bound inference workload at 1000 QPS. The min_replicas=1 ensures a baseline capacity, while max_replicas=5 allows scaling to handle spikes without over-provisioning, keeping p99 under 100 ms.

Answer

GPU-enabled machine with min_replicas=1 and max_replicas=2

Answer

n1-standard-8 with min_replicas=3 and max_replicas=3 (fixed)

Answer

n1-highmem-2 with min_replicas=2 and max_replicas=10

Question 10

What is the primary purpose of Vertex AI Edge Manager?

Accepted Answer

To deploy and manage ML models on edge devices at scale. Vertex AI Edge Manager is specifically designed to deploy, monitor, and manage ML models on edge devices at scale. It handles model packaging, over-the-air updates, and health monitoring across fleets of edge devices, which is distinct from simply running batch predictions or converting model formats.

Answer

To run batch predictions on edge devices

Answer

To convert models to TensorFlow Lite automatically

Answer

To train models on edge devices using federated learning

Question 11

A company uses Vertex AI Vector Search (Matching Engine) for a product recommendation system. The product embeddings are updated hourly. Which index update method should they use to ensure low latency for new items?

Accepted Answer

Use streaming updates to add new embeddings incrementally. Vertex AI Vector Search supports streaming updates, allowing new embeddings to be added incrementally without rebuilding the entire index. This ensures low latency for new items by making them searchable almost immediately after update, which is critical for hourly refresh cycles where batch rebuilds would introduce significant delay.

Answer

Batch rebuild the index every hour

Answer

Create a new index each hour and swap endpoints

Answer

Use brute-force index to simplify updates

Question 12

A company is deploying a complex model that requires GPU for inference. They want to use Vertex AI for serving. Which TWO steps are required to deploy the model with GPU support? (Choose 2)

Accepted Answer

Select a GPU-enabled machine type such as n1-standard-4 with 1 x NVIDIA Tesla T4.. Vertex AI requires selecting a GPU-enabled machine type (e.g., n1-standard-4 with 1 x NVIDIA Tesla T4) when deploying a model for inference. This is done in the machine specification of the endpoint deployment, ensuring the GPU hardware is allocated for the serving container.

Answer

Enable Vertex AI Model Optimization for automatic GPU compilation.

Answer

Increase the minimum replicas to at least 2 for GPU redundancy.

Answer

Use gRPC protocol for prediction requests to reduce latency.

Question 13

An ML engineer needs to deploy a model to Vertex AI for online predictions and enable autoscaling to zero when not in use. Which THREE conditions must be met? (Choose 3)

Accepted Answer

Enable serverless serving by selecting the appropriate serving mode.. Vertex AI offers a serverless serving mode that automatically scales resources to zero when no requests are being processed. By selecting this mode, the ML engineer enables the endpoint to scale down completely during idle periods, eliminating costs for unused infrastructure. This is distinct from standard serving, which maintains a minimum number of replicas.

Answer

Use a GPU-enabled machine type for faster scaling.

Answer

Deploy the model using a custom container with WebSockets support.

Question 14

Which TWO of the following are benefits of using Vertex AI Matching Engine (Vector Search) over a brute-force nearest neighbor search? (Choose 2)

Accepted Answer

Lower query latency for large datasets. Vertex AI Matching Engine uses approximate nearest neighbor (ANN) algorithms like ScaNN (Scalable Nearest Neighbors) to index high-dimensional vectors. For large datasets, ANN dramatically reduces query latency by avoiding a full scan of all vectors, unlike brute-force search which must compute distances against every vector. This makes option C correct because ANN trades a negligible accuracy loss for orders-of-magnitude faster retrieval.

Answer

Simpler to implement than brute-force

Answer

Supports real-time updates without index rebuild

Answer

Guaranteed exact nearest neighbor results

Question 15

A company is migrating from an on-premises ML serving infrastructure to Vertex AI. They have multiple models that need to be served from the same endpoint with different traffic percentages. They also need to monitor prediction quality. Which THREE actions should they take? (Choose 3)

Accepted Answer

Deploy multiple model versions on the same endpoint with traffic_split parameter.. Vertex AI endpoints support deploying multiple model versions and using the `traffic_split` parameter to distribute traffic percentages among them. This allows the company to serve different models from a single endpoint while controlling the proportion of requests each model receives, meeting the requirement for a unified serving infrastructure.

Answer

Deploy each model as a separate endpoint and use Cloud Load Balancing.

Answer

Export logs to BigQuery for manual analysis only.