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Navigating the GCP-ACE Exam: Deploying Machine Learning Models
Learn how to tackle a tricky GCP-ACE exam question on deploying machine learning models with the right Google Cloud service.
In the Google Associate Cloud Engineer (GCP-ACE) exam, candidates often stumble over deployment questions related to machine learning models. Choosing the correct service can seem daunting, especially when multiple options appear suitable at first glance.
The question
Your company wants to deploy a machine learning model on Google Cloud that can automatically scale based on incoming traffic. Which service would be the most suitable for this requirement?
- A. App Engine
- B. Cloud Functions
- C. AI Platform Prediction
- D. Compute Engine
Think before you scroll
Before choosing an answer, consider the specific needs of deploying a machine learning model, particularly the requirement for automatic scaling. Each option provides different capabilities, which can affect the performance and management of your model.
The answer
The correct option is C. AI Platform Prediction. This service is specifically designed for serving machine learning models and has built-in functionality to automatically scale based on incoming traffic. This makes it the most suitable choice for deploying models effectively.
Why the other options lose
- A. App Engine: While App Engine can scale applications, it is a general-purpose platform designed for web applications and not specifically optimized for machine learning model deployment. It lacks the specialized features of AI Platform Prediction, which are essential for model serving.
- B. Cloud Functions: Cloud Functions is an event-driven serverless compute service. Although it can scale automatically in response to events, it is not tailored for hosting and serving machine learning models. It is better suited for lightweight, stateless functions rather than complex model deployments.
- D. Compute Engine: Compute Engine provides virtual machines that can be configured for various workloads. However, it requires manual scaling configurations, making it less suitable for scenarios where automatic scaling is needed for machine learning model serving.
The concept behind it
The principle here is understanding the specific purposes of Google Cloud services. AI Platform Prediction is tailored for machine learning and offers features like automatic scaling and model management. Recognizing the intended use case of each service is crucial for making informed decisions in your exam.
Exam trap to remember
Remember: choose services that are specifically designed for the task at hand, especially when deployment and scaling are critical. AI Platform Prediction is your go-to for machine learning models.