PMLE Serving and Scaling Models Practice Test 4 — 15 Questions

Question 1

Which TWO of the following can be used as input sources for Vertex AI batch prediction jobs? (Choose 2)

Accepted Answer

BigQuery. BigQuery is a supported input source for Vertex AI batch prediction jobs because Vertex AI can directly read data from BigQuery tables for batch predictions. This integration allows you to store your prediction requests in BigQuery and have Vertex AI write the predictions back to a BigQuery output table, streamlining the workflow for large-scale predictions without needing to export data to Cloud Storage first.

Answer

Cloud Firestore

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Cloud SQL

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Cloud Spanner

Question 2

You are deploying a model to a Vertex AI endpoint and need to minimize latency for online predictions. Which machine type should you choose?

Accepted Answer

n1-standard-2 with NVIDIA Tesla T4. GPU-enabled machines (e.g., n1-standard-2 with NVIDIA Tesla T4) accelerate compute-heavy models, reducing prediction latency significantly compared to CPU-only instances.

Answer

e2-standard-2

Answer

n1-standard-2

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n1-highmem-2

Question 3

You need to perform batch predictions on 10 TB of data stored in BigQuery using Vertex AI. The model requires some preprocessing that cannot be expressed in SQL. What is the most scalable approach?

Accepted Answer

Use Dataflow to read from BigQuery, perform preprocessing, write results to GCS, then run Vertex AI batch prediction job with GCS source.. Dataflow (Apache Beam) provides a fully managed, auto-scaling, serverless execution environment that can read from BigQuery, apply arbitrary Python/Java preprocessing logic (e.g., feature engineering, normalization) that cannot be expressed in SQL, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction can then read from GCS as input, making this the most scalable approach for 10 TB of data without requiring custom model container changes or manual data movement.

Answer

Use a Cloud Function to preprocess each row and write to a new BigQuery table, then run batch prediction.

Answer

Use Vertex AI batch prediction with BigQuery source and include preprocessing logic in the model container.

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Export BigQuery data to CSV, run a local Python script for preprocessing, then upload to GCS and start a batch prediction job.

Question 4

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.. Streaming updates allow real-time insertion of new embeddings without rebuilding the entire index, maintaining low latency for queries.

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 5

You have a Vertex AI endpoint serving a model with min replicas=2 and max replicas=10. You notice that during low traffic hours, the endpoint still runs 2 replicas, incurring costs. You want to reduce costs to zero when there is no traffic. What should you do?

Accepted Answer

Change min replicas to 0 and max replicas to 10.. Setting min replicas to 0 allows Vertex AI to scale down to zero instances when there is no traffic, eliminating costs during idle periods. The endpoint will automatically scale up from 0 to handle incoming requests, while max replicas=10 ensures it can handle peak load. This is the standard approach for cost optimization in Vertex AI endpoints.

Answer

Use a custom metric to trigger scaling down to zero.

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Delete the endpoint when not in use and recreate it on demand.

Answer

Set max replicas to 0.

Question 6

You are A/B testing a new model version (challenger) against the current version (champion) on Vertex AI. You want to gradually shift traffic from champion to challenger while measuring business metrics. Which approach should you use?

Accepted Answer

Deploy both models to the same endpoint and use the traffic split feature to allocate percentages.. Vertex AI endpoints support traffic splitting between deployed models. By updating the traffic percentage, you can gradually shift traffic and monitor performance.

Answer

Deploy the challenger to a separate endpoint and use a load balancer to route a percentage of requests.

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Use Cloud Armor to route traffic based on headers.

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Create a new endpoint for the challenger and gradually shift DNS records.

Question 7

You need to serve multiple models on a single Vertex AI endpoint to reduce costs. How can you achieve this?

Accepted Answer

Use Vertex AI Prediction with multi-model serving by deploying multiple models to one endpoint with traffic splits.. Vertex AI Prediction supports multi-model serving, allowing you to deploy multiple models to a single endpoint and use traffic splits to route a percentage of requests to each model. This reduces costs by sharing underlying infrastructure (e.g., compute resources) across models, rather than provisioning separate endpoints or containers for each model.

Answer

Use Cloud Run to serve each model separately.

Answer

Package all models into a single container and deploy that container.

Answer

Deploy each model to its own endpoint and use a load balancer.

Question 8

You are deploying a PyTorch model on Vertex AI and want to use NVIDIA Triton Inference Server for optimal performance. You have built a custom container with Triton. Which serving configuration should you use?

Accepted Answer

Upload your Triton container to Container Registry and specify it as the prediction container in Vertex AI Model.. Vertex AI supports custom containers for prediction. You can bring your own container with Triton installed, and Vertex AI will deploy it to the endpoint.

Answer

Deploy the model on GKE with Triton and expose via Istio.

Answer

Use the prebuilt Vertex AI PyTorch prediction container and set environment variables to enable Triton.

Answer

Use Vertex AI Model Optimization to automatically convert the model to TensorRT and deploy with built-in server.

Question 9

You are using Vertex AI batch prediction and your model requires preprocessing that involves joining two BigQuery tables. The preprocessing logic is complex and must be done before inference. How should you design the pipeline?

Accepted Answer

Use Dataflow to read from both BigQuery tables, perform the join and preprocessing, write the results to GCS, then run Vertex AI batch prediction with GCS source.. Dataflow (Apache Beam) is designed for complex, stateful data processing like joining two BigQuery tables and performing custom preprocessing. It can read from BigQuery, execute the join logic, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction then reads the preprocessed data from GCS, which is the recommended pattern for non-trivial transformations before inference, as it decouples preprocessing from prediction and avoids resource contention.

Answer

Write a Cloud Composer workflow that runs the preprocessing and then triggers the batch prediction job.

Answer

Use Vertex AI batch prediction with a custom container that includes logic to read and join tables on the fly.

Answer

Use BigQuery to create a materialized view that joins the tables and directly use that as the batch prediction source.

Question 10

You have a Vertex AI endpoint with autoscaling enabled. You notice that during traffic spikes, the endpoint takes a long time to scale up, causing prediction errors. What is the most effective solution?

Accepted Answer

Increase the minimum number of replicas to maintain a larger buffer.. Setting higher min replicas ensures a baseline of compute always available to absorb traffic spikes, reducing cold start latency. Increasing max replicas allows scaling higher but does not address the initial delay.

Answer

Use a larger machine type to handle more requests per replica.

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Reduce the target CPU utilization to trigger scaling earlier.

Answer

Increase the maximum number of replicas.

Question 11

You need to deploy a TensorFlow model to edge devices for real-time inference with minimal latency. The model is currently trained on Vertex AI. Which approach should you use?

Accepted Answer

Convert the model to TensorFlow Lite using the TF Lite converter, then deploy to edge devices via Vertex AI Edge Manager.. TensorFlow Lite is specifically designed for on-device inference on edge devices, offering reduced model size and optimized performance for low-latency, real-time scenarios. Vertex AI Edge Manager provides a managed service to deploy, monitor, and update models on edge devices, making it the ideal combination for this use case.

Answer

Export the model as a SavedModel and deploy it to Vertex AI Edge Manager using the Edge Manager API.

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Deploy the model to Vertex AI endpoint and use Cloud IoT Core to stream data to the cloud for inference.

Answer

Use Vertex AI Model Optimization to quantize the model to INT8 and then deploy as a web service on a Raspberry Pi.

Question 12

Your Vertex AI endpoint receives many identical prediction requests (same input features). You want to cache responses to reduce latency and cost. Which Google Cloud service should you use?

Accepted Answer

Cloud Memorystore for Redis. Cloud Memorystore (Redis) is ideal for caching prediction results. By hashing the request input, you can store and retrieve cached responses, avoiding redundant inference.

Answer

Cloud CDN

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Bigtable

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Cloud Storage with object versioning

Question 13

You are using Vertex AI Vector Search to find nearest neighbors for a recommendation system. Your index is built on 10M embeddings and you need low-latency queries. You want to ensure that adding new embeddings does not require a full index rebuild. Which index type should you use?

Accepted Answer

ANN index with the 'streaming' update mode. The ANN index with 'streaming' update mode is correct because it supports real-time insertion of new embeddings without requiring a full index rebuild, which is essential for low-latency recommendation systems. This mode uses a separate unsearched buffer for new vectors and periodically merges them into the main index, enabling continuous updates while maintaining query performance.

Answer

Brute-force index (exact neighbor search)

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ANN index with the 'batch' update mode

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Tree-AH index

Question 14

You are optimizing a model for deployment on Vertex AI using NVIDIA Triton Inference Server. Which TWO actions can you take to improve inference performance?

Accepted Answer

Use TensorRT to quantize the model to FP16 or INT8.. TensorRT optimizes model inference by quantizing weights and activations to lower precision formats like FP16 or INT8, reducing memory bandwidth and computation time without significant accuracy loss. This is a standard technique for improving throughput on NVIDIA GPUs, especially when deploying with Triton Inference Server, which natively supports TensorRT-optimized model repositories.

Answer

Increase the number of model replicas to the maximum.

Answer

Disable model caching to reduce memory usage.

Answer

Use a larger machine type with more vCPUs.

Question 15

You are deploying a model on Vertex AI and need to ensure high availability and low latency. Which THREE configurations should you implement?

Accepted Answer

Choose a machine type with GPUs for compute-intensive models.. GPUs are essential for compute-intensive models, such as deep neural networks, as they provide parallel processing capabilities that significantly reduce inference latency compared to CPUs. On Vertex AI, selecting a machine type with GPUs (e.g., n1-standard-4 with NVIDIA T4) ensures that the model can handle high-throughput requests with low latency, which is critical for real-time serving.

Answer

Use a custom endpoint with a static IP address.

Answer

Deploy to a single zone to reduce network latency.