PMLE Practice Test 30 — 15 Questions

Question 1

A company wants to implement continuous delivery (CD) for ML models, where a model is automatically deployed to a staging environment and only promoted to production after passing an evaluation gate. Which combination of GCP services is BEST suited for orchestrating this CD pipeline?

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

Accepted Answer

Cloud Build with Vertex AI Pipelines and Cloud Deploy. Cloud Build can trigger on code/model changes and run a pipeline that deploys to staging. After evaluation, if successful, it can promote to production using Cloud Deploy or directly update Vertex AI endpoints. Cloud Composer (Airflow) is also a good option for complex orchestration, but for CI/CD, Cloud Build is a natural fit. The combination of Cloud Build, Cloud Deploy, and Vertex AI provides a robust CD pipeline.

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Cloud Scheduler and Pub/Sub

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Cloud Composer (Airflow) with Cloud Functions

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Vertex AI Pipelines with Cloud Run

Question 2

A team uses Vertex AI Feature Store for storing features. They want to share feature definitions with other teams in a collaborative manner. What is the best way to collaborate on feature definitions?

Accepted Answer

Use a shared repository with feature definition files and CI/CD to update the feature store.. Using a shared repository with feature definition files and CI/CD pipelines enables version control, peer review, and automated deployment to Vertex AI Feature Store. This approach ensures consistency, traceability, and collaboration without risking direct, uncoordinated changes to the production feature store.

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Grant all teams write access to the same feature store so they can modify definitions directly.

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Export the feature definitions as CSV and email them to the other teams.

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Use a wiki page to document feature definitions and update it manually.

Question 3

Refer to the exhibit. An alert policy is configured to trigger when prediction latency exceeds 500 ms for 5 consecutive minutes. The team is experiencing many false positive alerts during brief latency spikes. Which adjustment would most effectively reduce false positives while still detecting prolonged latency issues?

Accepted Answer

Increase the duration to 30 minutes. Increasing the duration from 5 to 30 minutes (Option C) directly addresses the problem of false positives from brief latency spikes by requiring the latency to exceed 500 ms for a longer continuous period before triggering an alert. This ensures that only sustained, prolonged latency issues—not transient spikes—activate the policy, aligning with the goal of detecting genuine degradation while ignoring noise.

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Change the comparison to less than

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Add a condition that CPU utilization is also high

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Increase the threshold to 1000 ms

Question 4

A team deploys a PyTorch model on Vertex AI for online predictions. They notice that after deployment, the latency increases over time, especially during peak hours. The model is served using a custom container. What is the most likely cause?

Accepted Answer

The prediction requests are not being batched, and the model inference code is not optimized for concurrency.. The latency increase over time, especially during peak hours, indicates that the model inference code is not handling concurrent requests efficiently. Without batching or optimized concurrency, each request is processed sequentially, causing a queue buildup under load. This is a common issue with custom containers on Vertex AI when the prediction handler is single-threaded or lacks async processing.

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The custom container does not have a health check, causing instances to be prematurely terminated.

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The model is not using GPU even though a GPU machine is selected.

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The model is too large for the machine's memory, causing swapping.

Question 5

A data scientist wants to track the lineage of a dataset used in a training run. Which Vertex AI feature should they use?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the correct choice because it is specifically designed to track the lineage of datasets, models, and other artifacts throughout the ML lifecycle. It records metadata about each step in a pipeline, including the source dataset used for a training run, enabling full provenance tracking. This allows data scientists to trace back which data was used, how it was transformed, and which model version it produced.

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Vertex AI Feature Store

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Vertex AI Experiments

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Vertex AI Model Registry

Question 6

Which THREE of the following are valid ways to share a Vertex AI model across two different Google Cloud projects?

Accepted Answer

Use Vertex AI Model Registry's cross-project sharing feature with IAM conditions.. Vertex AI Model Registry supports cross-project sharing by allowing you to grant IAM roles with conditions on the model resource. This enables a model registered in one project to be accessed by a service account from another project without moving or copying the model artifacts.

Answer

Publish the model to Google Cloud Marketplace.

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Use the gcloud ai models copy command to copy the model across projects.

Question 7

A team uses Vertex AI Pipelines to automate training and deployment. They need to ensure that only models that pass a set of quality checks (e.g., accuracy > 0.9, latency < 100ms) are deployed to production. How should they implement this?

Accepted Answer

Add a Pipeline component that evaluates metrics and uses a conditional gate to deployment. Vertex AI Pipelines can include custom components to evaluate metrics and conditionally proceed to deployment if thresholds are met. Option D correctly adds a Pipeline component that evaluates metrics and uses a conditional gate to deployment. Option A is manual and not automated. Option B lacks conditional logic. Option C deploys every model without checks.

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Manually review each model before promotion

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Use Cloud Functions to deploy only if accuracy is reported in BigQuery

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Set up Cloud Build triggers to deploy every model version

Question 8

An organization needs to serve a large model (10 GB) with low latency across multiple regions. Which Vertex AI feature best meets this requirement?

Accepted Answer

Global endpoints. Global endpoints, is correct because Vertex AI Global Endpoints automatically route prediction requests to the nearest region with capacity, reducing latency for geographically distributed users. Option A, Private endpoints, is for secure VPC access, not multi-region latency. Option B, Batch prediction, is for offline batch jobs, not real-time serving. Option C, Model Monitoring, is for observing model performance and drift, not for serving.

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Private endpoints

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Batch prediction

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Model Monitoring

Question 9

A startup wants to add sentiment analysis to their customer feedback app without any labeled data or custom model training. Which Google Cloud service should they use?

Accepted Answer

Cloud Natural Language API. The Cloud Natural Language API provides pre-trained models for sentiment analysis that require no labeled data or custom training. It offers a ready-to-use sentiment analysis feature via a simple API call, making it ideal for a startup that wants to add sentiment analysis without any machine learning expertise or data preparation.

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AutoML Natural Language with manual labeling

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Use BigQuery ML to train a text classification model

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Train a custom sentiment model on Vertex AI

Question 10

A startup wants to deploy a small machine learning model for real-time predictions but has a very limited budget. Traffic is minimal and predictable. They want to avoid paying for idle resources. Which serving option is most cost-effective?

Accepted Answer

Deploy the model as a Cloud Run service using a custom container.. Cloud Run with a custom container is the most cost-effective option because it automatically scales to zero when idle, meaning you pay only when requests come in. For a small model with minimal, predictable traffic, this avoids idle costs entirely. Option A (single Compute Engine VM with GPU) incurs 24/7 cost and is overkill for a small model. Option B (Vertex AI Batch Prediction) is designed for asynchronous batch jobs, not real-time predictions. Option D (Vertex AI Endpoint with min_replica_count=0) can also scale to zero, but requires additional setup and may involve higher per-request costs compared to Cloud Run; moreover, Cloud Run is simpler and more cost-effective for lightweight models.

Answer

Deploy the model on a single Compute Engine VM with a GPU.

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Use Vertex AI Batch Prediction for each prediction request.

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Deploy the model to Vertex AI Endpoint with min_replica_count=0.

Question 11

A team wants to serve a large PyTorch model (3 GB) for online predictions with low latency. Which THREE actions should they take?

Accepted Answer

Use a custom container that preloads the model into memory.. For online low-latency predictions with a large model, preloading the model into memory via a custom container (A) eliminates cold-start latency. GPU acceleration (C) significantly speeds up inference for large models. Optimizing with TorchScript or quantization (D) reduces model size and inference time. Batch prediction (B) is for offline batch processing, not online. Multi-region deployment (E) improves availability and global latency but does not directly reduce prediction latency for a single request.

Answer

Use batch prediction instead of online prediction.

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Deploy in multiple regions with Cloud Load Balancing.

Question 12

A company wants to deploy a model for real-time inference with high availability across multiple Google Cloud regions. The model is small and stateless. Which two steps should they take? (Choose two.)

Accepted Answer

Deploy the model to Vertex AI Prediction endpoints in multiple regions and use a global external HTTP(S) load balancer to route traffic to the nearest region.. Options A and B are correct because they both deploy the model across multiple regions behind a global HTTP(S) load balancer, providing high availability and regional failover for real-time inference. Option C is wrong because Cloud Functions is region-specific and not designed for latency-sensitive, multi-region inference; it lacks the routing capabilities needed for global failover. Option D is wrong because multiple replicas within a single region cannot survive a regional outage. Option E is wrong because a single region with a global load balancer still has a single point of failure at the endpoint region.

Answer

Use Cloud Functions with a global HTTP(S) load balancer.

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Use a single Vertex AI Prediction endpoint with multiple replicas across zones in the same region.

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Deploy the model to a Vertex AI Prediction endpoint in a single region and use a global external HTTP(S) load balancer.

Question 13

Which TWO tools can be used to collaborate on feature definitions across teams?

Accepted Answer

Vertex AI Feature Store. The correct options are B (Vertex AI Feature Store) and E (Data Catalog). Vertex AI Feature Store allows teams to share and reuse feature definitions across projects. Data Catalog provides metadata management and can catalog feature definitions, making them discoverable and understandable across teams. Cloud Storage (A) is a blob storage service, not a feature collaboration tool. Cloud Build (D) is for CI/CD pipelines. Cloud Logging (C) is for log management.

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Cloud Storage

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Cloud Logging

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Cloud Build

Question 14

An ML engineer is scaling a prototype to production using Vertex AI Pipelines. The pipeline includes data validation, preprocessing, training, and deployment steps. They want to ensure that the pipeline can be reproduced and audited. What is the best practice?

Accepted Answer

Define the pipeline using Kubeflow Pipelines SDK and run it on Vertex AI Pipelines.. Vertex AI Pipelines is a fully managed service that automatically tracks artifacts, parameters, and lineage, ensuring reproducibility and auditability. Option A uses the Kubeflow Pipelines SDK to define the pipeline and runs it on Vertex AI Pipelines, which provides built-in tracking. Option B (Docker with fixed tags) lacks automated lineage tracking. Option C (no versioning) loses audit trail. Option D (requirements.txt only) addresses dependencies but not pipeline orchestration or artifact tracking.

Answer

Use a Docker container with fixed tags and manually record runs.

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Store all data and models in a single Cloud Storage bucket with no versioning.

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Pin all library versions in a requirements.txt file.

Question 15

A company is deploying a deep learning model on edge devices with limited storage and computational resources. They need to reduce the model size by 80% while maintaining acceptable accuracy. Which two techniques should they combine?

Accepted Answer

Quantization and pruning. Quantization reduces model size by converting weights from FP32 to INT8, achieving up to 75% size reduction. Pruning removes redundant weights (e.g., 50% pruning) to further shrink the model. Together, these two techniques can reduce model size by over 80% (e.g., 50% pruning + INT8 quantization yields ~87.5% reduction) while preserving acceptable accuracy on edge devices. Knowledge distillation also reduces size but typically requires a pre-trained teacher and may not achieve the extreme compression needed when combined with quantization alone.

Answer

Pruning and distillation

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Quantization and distillation