PMLE Serving and Scaling Models Practice Test 6 — 15 Questions

Question 1

You deployed a model to a Vertex AI endpoint with minReplicas=0 and maxReplicas=5. After sending prediction requests, you notice the endpoint takes about 30 seconds to respond initially, but subsequent requests are fast. What is the most likely cause?

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

Accepted Answer

Cold start occurs because the endpoint scaled down to zero.. Vertex AI endpoints with minReplicas=0 scale down to zero when idle. The first request after a period of inactivity triggers a cold start, where the endpoint must provision a new VM instance and load the model, causing a ~30-second delay. Subsequent requests are fast because the instance remains warm and handles them without provisioning overhead.

Answer

The model is too large for the machine type.

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The VPC Service Controls are blocking the initial request.

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The endpoint's autoscaling is misconfigured.

Question 2

You have a champion model serving 100% traffic on a Vertex AI endpoint. You want to deploy a challenger model and gradually shift 10% of traffic to it for A/B testing. What is the correct approach?

Accepted Answer

Deploy the challenger on the same endpoint and use the traffic split parameter to allocate 10% traffic to it.. Vertex AI endpoints support traffic splitting by deploying multiple model versions and assigning traffic percentages. You deploy the challenger as a new deployed model on the same endpoint and set traffic split: champion 90%, challenger 10%.

Answer

Use Cloud Run to deploy both models and use Cloud Endpoints for traffic splitting.

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Deploy the challenger on a separate endpoint and use Cloud Armor to split traffic.

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Create a new endpoint for the challenger and route 10% of requests via a load balancer.

Question 3

You need to run batch predictions on 10 TB of text data stored in BigQuery using a custom container model hosted in Vertex AI. What is the most cost-effective and simple approach?

Accepted Answer

Use Vertex AI batch prediction with BigQuery source and sink.. Vertex AI batch prediction natively supports BigQuery as both source and sink, allowing you to run predictions on 10 TB of text data without any data movement or intermediate storage. This is the most cost-effective and simple approach because it eliminates the need for exporting data, managing infrastructure, or calling online endpoints, and it leverages Vertex AI's optimized batch inference infrastructure that scales automatically.

Answer

Use Cloud Run jobs to read from BigQuery and write results back.

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Export BigQuery data to GCS, then run a Dataflow pipeline to call the model's online prediction endpoint for each row.

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Use Cloud Dataproc to spin up a Spark cluster and run the model inference in parallel.

Question 4

Your team is deploying a large recommendation model on Vertex AI endpoints using GPUs. You need to minimise latency while optimising cost. The model serves many similar requests from the same users within short time windows. Which additional service would best reduce latency and cost?

Accepted Answer

Use Cloud Memorystore to cache prediction results.. Cloud Memorystore (Redis) in front of the Vertex AI endpoint lets you cache prediction results keyed by user/request signature, so repeated similar requests within short windows are served from cache instead of hitting the GPU-backed model. This reduces both latency (cache hit is sub-millisecond) and cost (fewer GPU inference calls).

Answer

Switch to CPU-only instances to reduce cost.

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Increase maxReplicas to handle the load without caching.

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Set up a Cloud CDN in front of the endpoint.

Question 5

You need to deploy a PyTorch model for online inference on Vertex AI but the model was trained using custom ops that are not natively supported. You want to use NVIDIA Triton Inference Server for optimisation. How should you proceed?

Accepted Answer

Build a custom container with NVIDIA Triton Inference Server and deploy it to Vertex AI.. Vertex AI supports custom containers for prediction, so you can package NVIDIA Triton Inference Server with your PyTorch model and any custom ops/libraries it needs, then deploy that container as a Vertex AI Model. Triton natively supports PyTorch (via TorchScript), ONNX, TensorRT, and custom backends, making it the right choice when the model relies on non-standard operators that Vertex AI's pre-built PyTorch/TensorFlow containers cannot execute.

Answer

Convert the model to TFLite and deploy on an edge device.

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Export the model to ONNX and deploy using Vertex AI's built-in TensorFlow serving.

Answer

Use Vertex AI Model Optimisation to automatically quantise the model.

Question 6

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 7

You need to query a Vertex AI Vector Search index for nearest neighbours. The index is deployed on an endpoint. Which API method should you use to perform the query?

Accepted Answer

projects.locations.indexEndpoints.findNeighbors. The correct API method to query a deployed Vertex AI Vector Search index for nearest neighbors is `projects.locations.indexEndpoints.findNeighbors`. This method is specifically designed for vector similarity search against an index endpoint, returning the nearest neighbors for a given query vector. The other options either target the wrong resource (indexes instead of indexEndpoints) or use methods intended for different purposes like model prediction.

Answer

projects.locations.indexes.match

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projects.locations.indexes.query

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projects.locations.endpoints.predict

Question 8

A data science team needs to serve multiple versions of the same ML model on Vertex AI Endpoints for A/B testing. They want to gradually shift traffic from the current 'champion' model to a new 'challenger' model. Which feature should they use?

Accepted Answer

Deploy both models to the same endpoint and use traffic splitting.. Vertex AI Endpoints natively support traffic splitting, allowing you to deploy multiple model versions (e.g., champion and challenger) to the same endpoint and assign a percentage of traffic to each. This enables gradual A/B testing without additional infrastructure, as the endpoint automatically routes requests based on the configured split. Option C is correct because it leverages this built-in feature, which is designed specifically for this use case.

Answer

Deploy the challenger to a separate endpoint and use a proxy to split traffic.

Answer

Use Cloud Load Balancing with weighted backend services.

Answer

Use Vertex AI Experiments to manage model versions.

Question 9

Which Vertex AI service is designed for building and managing approximate nearest neighbor (ANN) indexes for similarity search at scale?

Accepted Answer

Vertex AI Matching Engine (Vector Search). Vertex AI Matching Engine (Vector Search) is specifically designed for building and managing approximate nearest neighbor (ANN) indexes for similarity search at scale. It allows you to create indexes from embeddings and perform low-latency similarity queries. This service is optimized for large-scale vector search use cases.

Answer

Vertex AI AutoML

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Vertex AI Workbench

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Vertex AI Prediction

Question 10

A company wants to run batch predictions on millions of records stored in BigQuery. They need to preprocess the data (e.g., feature engineering) before feeding it to the model. Which approach is most scalable and cost-effective?

Accepted Answer

Preprocess with Cloud Dataflow, output to Cloud Storage, then submit a Vertex AI batch prediction job.. The most scalable and cost-effective because Cloud Dataflow (Apache Beam) provides serverless, auto-scaling preprocessing that handles large volumes of data efficiently, and Vertex AI batch predictions natively read from Cloud Storage, avoiding the need to manage infrastructure. This decouples preprocessing from prediction, allowing each to scale independently and minimizing costs by using ephemeral, pay-per-use resources.

Answer

Use a large DataProc cluster to preprocess and run batch predictions.

Answer

Preprocess inline in the batch prediction job using a custom container.

Answer

Use a custom Python script on a Compute Engine instance.

Question 11

Which of the following is a benefit of using Vertex AI Endpoints with autoscaling and scale-to-zero?

Accepted Answer

It reduces costs by scaling down to zero replicas when no requests are received.. Vertex AI Endpoints with autoscaling and scale-to-zero allow the number of serving replicas to dynamically adjust based on incoming traffic. When no requests are received, the endpoint can scale down to zero replicas, meaning you are not charged for idle compute resources. This directly reduces operational costs compared to maintaining a minimum number of always-on instances.

Answer

It eliminates the need for a load balancer.

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It reduces model training time.

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It automatically upgrades the model version.

Question 12

An engineer deploys a model to a Vertex AI endpoint with minReplicas=1 and maxReplicas=3. The endpoint receives a sudden traffic spike, but it does not scale up beyond 1 replica. The CPU utilization target is 60%. What is the most likely cause?

Accepted Answer

The CPU utilization is below the target threshold, so the autoscaler does not add replicas.. Vertex AI's autoscaler uses CPU utilization as a metric to decide when to add replicas. If the CPU utilization remains below the 60% target threshold, the autoscaler will not trigger scale-up, even during a traffic spike. The endpoint is configured with minReplicas=1 and maxReplicas=3, but without exceeding the target, it stays at the minimum.

Answer

The model is not deployed correctly.

Answer

The endpoint is configured with the wrong machine type.

Answer

The endpoint is using GPU which cannot autoscale.

Question 13

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

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Hash-based index

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Tree-based index

Question 14

Which API is recommended for high-throughput, low-latency online prediction requests to Vertex AI endpoints?

Accepted Answer

gRPC API. gRPC API is recommended for high-throughput, low-latency online prediction requests to Vertex AI endpoints because it uses HTTP/2 for multiplexed streaming, binary serialization (Protocol Buffers), and supports bidirectional streaming, which reduces latency and improves throughput compared to REST. Vertex AI's prediction service natively supports gRPC for real-time inference, making it the optimal choice for latency-sensitive applications.

Answer

Cloud Functions

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REST API

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Cloud Pub/Sub

Question 15

An organization wants to deploy a TensorFlow model on edge devices such as smartphones and IoT devices for offline inference. Which format should they export the model to?

Accepted Answer

TensorFlow Lite (TFLite). TensorFlow Lite (TFLite) is Google's lightweight runtime and model format specifically designed for on-device inference on mobile and embedded/IoT hardware. It produces a compact .tflite flatbuffer optimized for low latency, small binary size, and minimal memory, and supports hardware acceleration via delegates (GPU, NNAPI, Edge TPU). Exporting to TFLite is the standard path for offline inference on smartphones and IoT devices.

Answer

ONNX format

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SavedModel format

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HDF5 format