PMLE Serving and Scaling Models Practice Test 9 — 15 Questions

Question 1

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.

Answer

Brute-force (flat) index

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Tree-based index

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Hashing-based index

Question 2

A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)

Accepted Answer

Deploy the model on a GPU-enabled machine. Option B is correct because deploying the model on a GPU-enabled machine type gives the inference workload hardware acceleration, which speeds up the matrix/tensor computations typical of ML models and thereby reduces per-request prediction latency. Option C is correct because enabling model quantization via Vertex AI Model Optimization reduces the numerical precision of weights and activations (for example to INT8), shrinking model size and memory bandwidth requirements and enabling faster inference, which lowers latency. Option A is not correct because increasing the maximum number of replicas improves throughput and availability under load, but it does not reduce the latency of an individual prediction. Option D is not correct because using a smaller machine type with less memory would likely increase latency or cause out-of-memory failures rather than reduce it. Option E is not correct because autoscaling with a lower target CPU utilization only triggers scaling sooner to add capacity; it addresses throughput/load handling, not the intrinsic latency of a single inference.

Answer

Increase the maximum number of replicas

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Use a smaller machine type with less memory

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Enable autoscaling with a lower target CPU utilization

Question 3

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. Option B is correct because Vertex AI Matching Engine supports streaming updates, which allow the index to be modified incrementally as new data arrives without requiring a full rebuild, directly satisfying the requirement for frequent updates in a real-time recommendation system. Option D is correct because deploying the index to a Vertex AI Matching Engine endpoint is the standard mechanism for serving low-latency, real-time nearest-neighbor queries at scale, which is exactly what the scenario demands. Option A is not appropriate because storing the index in Cloud Storage and querying it via Python does not provide the managed, low-latency serving infrastructure of Matching Engine. Option C is not appropriate because a brute-force index performs exhaustive comparisons, which is far too slow for low-latency real-time serving at scale. Option E is not appropriate because batch-only updates cannot keep pace with frequent updates and would introduce staleness in a real-time recommendation system.

Answer

Store the index in Cloud Storage and query via Python

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Use a brute-force index for exact results

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Use batch updates only

Question 4

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.

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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 5

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.

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Vertex AI Prediction Endpoint

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Vertex AI Model Monitoring

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Vertex AI Feature Store

Question 6

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 natively supports BigQuery as both input source and output sink, so the engineer can submit a batch job directly against the BigQuery table and have predictions written back to BigQuery without any data movement code. This is the most efficient and least error-prone option for 10 TB of data.

Answer

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

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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 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

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Vertex AI Matching Engine

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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.

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

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

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Use Cloud CDN in front of the endpoint.

Question 9

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

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To convert models to TensorFlow Lite automatically

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To train models on edge devices using federated learning

Question 10

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.

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Batch rebuild the index every hour

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Create a new index each hour and swap endpoints

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Use brute-force index to simplify updates

Question 11

A team is building a batch prediction pipeline that processes raw data from Cloud Storage, performs complex preprocessing, and then runs predictions using a large model. The preprocessing step is compute-intensive and the prediction step is I/O-bound. Which TWO Google Cloud services should they combine to optimize cost and performance? (Choose 2)

Accepted Answer

Dataflow for preprocessing and writing results to Cloud Storage. Option A (Dataflow for preprocessing and writing results to Cloud Storage) is correct because Dataflow is a fully managed, autoscaling service built on Apache Beam that handles compute-intensive, parallel batch preprocessing over data in Cloud Storage, and it can write the transformed output back to Cloud Storage for the next stage. Option D (Vertex AI Batch Prediction with Cloud Storage source) is correct because it is the managed, I/O-bound batch inference service designed to read preprocessed files directly from Cloud Storage and run predictions on a large model without provisioning persistent serving infrastructure, which optimizes cost for batch workloads. Option B is wrong because Cloud Functions is event-driven and limited in execution time and memory, making it unsuitable for compute-intensive row-by-row preprocessing of large batch datasets. Option C is wrong because Cloud Run serving preprocessed data as an API adds unnecessary request/response overhead and is designed for online serving, not batch pipeline handoff. Option E is wrong because the pipeline explicitly reads raw data from Cloud Storage and writes preprocessed results there, so a BigQuery source for batch prediction does not match the described data flow.

Answer

Cloud Functions to preprocess data row by row

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Cloud Run to serve the preprocessed data as an API

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Vertex AI Batch Prediction with BigQuery source

Question 12

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.. Option A is correct because Vertex AI endpoints natively support serving multiple DeployedModels behind a single endpoint, and the traffic_split parameter on the endpoint lets you route configurable percentages of prediction traffic to each model version. Option C is correct because Vertex AI Model Monitoring is the managed service that detects training-serving skew and prediction drift on deployed models, directly addressing the requirement to monitor prediction quality. Option D is correct because prediction quality often depends on business outcomes that Model Monitoring does not capture, so Cloud Monitoring custom metrics let you track those outcome-based signals alongside Vertex AI metrics. Option B is not appropriate because separate endpoints with Cloud Load Balancing adds unnecessary complexity and does not provide Vertex AI's built-in traffic splitting or model monitoring integration. Option E is insufficient because exporting logs to BigQuery for manual analysis only is not an automated monitoring solution and does not satisfy the prediction-quality monitoring requirement.

Answer

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

Answer

Export logs to BigQuery for manual analysis only.

Question 13

A machine learning team deploys a PyTorch model for online prediction on Vertex AI using a custom container. They notice that the first few requests after scaling up experience high latency. What is the most likely cause and how should they mitigate it?

Accepted Answer

The container has a slow initialization; set initialDelaySeconds in the health probe to give more time before considering the pod ready.. The high latency on the first few requests after scaling up is a classic symptom of a slow container initialization. By setting `initialDelaySeconds` in the health probe, you allow the container more time to start up and become ready before it receives traffic, preventing premature routing that causes timeouts or retries. This is a common tuning parameter for custom containers on Vertex AI, where model loading or dependency initialization can take several seconds.

Answer

The endpoint is not configured for autoscaling; enable min_replica=0 to allow scale-to-zero.

Answer

The model file is corrupted; re-upload to Vertex AI Model Registry.

Answer

Use a smaller machine type (n1-standard-2) to reduce startup overhead.

Question 14

A retail company deploys a new recommendation model alongside the current champion on Vertex AI Endpoints. They want to gradually shift traffic to the challenger while monitoring business metrics (conversion rate). Which two steps are required? (Choose 2)

Accepted Answer

Deploy the challenger model to the same endpoint as the champion with a separate deployed model.. Option C is correct because Vertex AI Endpoints support multiple deployed models behind a single endpoint, so the challenger must be deployed as a separate DeployedModel (with its own model ID and deployed_model_id) on the same endpoint as the champion to enable side-by-side serving. Option D is correct because gradual traffic shifting is implemented by setting the endpoint's traffic_split map, which assigns percentage weights keyed by deployed_model_id (e.g., champion deployed model: 90, challenger deployed model: 10), allowing controlled canary rollout. Option A is not required because Vertex AI Experiments tracks training/experiment runs and metrics, not live endpoint traffic splitting. Option B is not required because Memorystore is a caching layer and does not perform traffic splitting or model comparison. Option E, while useful for observing conversion rate, is not a required step to shift traffic and is not marked correct; the question asks for the steps needed to perform the gradual shift itself.

Answer

Use Vertex AI Experiments to track the traffic split percentages.

Answer

Enable Cloud Memorystore to cache identical requests for both models.

Answer

Use Cloud Monitoring to track custom metrics like conversion rate per model version.

Question 15

A fintech company needs to deploy a TensorFlow model for real-time fraud detection with strict latency SLO (p99 < 100ms). They expect variable traffic with spikes. They also want to minimize cold-start latency. Which two configurations should they use? (Choose 2)

Accepted Answer

Use a GPU-enabled machine type (e.g., N1 with T4) to accelerate inference.. Option B is correct because a GPU-enabled machine type such as an N1 instance with an NVIDIA T4 accelerator provides the parallel compute throughput needed to keep TensorFlow inference within a p99 latency SLO under 100ms, which CPU-only serving often cannot guarantee for larger models. Option C is correct because setting min_replicas = 3 keeps a baseline of warm, already-loaded model instances, eliminating cold-start latency for the initial requests and giving the autoscaler headroom to absorb traffic spikes without waiting for new replicas to initialize. Option A is not appropriate because min_replicas = 0 enables scale-to-zero, which directly reintroduces cold-start latency and violates the strict p99 < 100ms SLO. Option D is not selected because Vertex AI Model Optimization (quantization) is a model-compression technique that may reduce latency but is not a required configuration for meeting the SLO and can degrade accuracy. Option E is not selected because batch prediction processes data offline in bulk and cannot serve real-time fraud detection requests with sub-100ms latency.

Answer

Set min_replicas = 0 to allow scale-to-zero and save costs.

Answer

Enable Vertex AI Model Optimization for automatic quantization.

Answer

Use batch prediction instead of online prediction.