AI-102: How to Create Custom Translation Models for Global E-Commerce Using Azure AI?

Learn how to implement Azure Custom Translator for specialized e-commerce translations – from workspace creation to model publishing. Discover the exact sequence for handling technical documents and industry jargon, with proven strategies from AI-102 certification experts. Includes workflow optimization for global platforms.

Question

Your organization, Nutex Inc., is developing a global e-commerce platform that requires accurate translations of specialized product descriptions and technical documents into multiple languages.

The standard translation models do not meet your quality requirements due to the specific jargon and terminology used in your industry. To address this, you decide to use Azure’s Custom Translator service to create a custom translation model that better suits your needs.

Arrange the process steps in the sequence you would follow to create a custom translation model.

Unordered Choices:

• Test the model.
• Publish the model.
• Create a workspace.
• Upload the documents.
• Create a project.
• Train the model.

Answer

Correct Order:

1. Create a workspace.
2. Create a project.
3. Upload the documents.
4. Train the model.
5. Test the model.
6. Publish the model.

Explanation

Below are the steps you would use to create a custom translation model:

1. Create a workspace – The first step in creating a custom translation model is to create a workspace. A workspace is a centralized environment in Azure where you can manage your projects, datasets, and models. You would log in to the Custom Translator to create a workspace (refer to the exhibit).
2. Create a project – After setting up the workspace, the next step is to create a project within that workspace. A project represents a specific translation task, such as translating product descriptions or technical documents.
3. Upload the documents – Once the project has been created, you need to upload the documents that will be used to train the custom translation model. These documents should include source texts along with their corresponding translations in the target language.
4. Train the model – With the documents uploaded, you can now proceed to train the custom translation model. Training involves the system analyzing the provided data to learn the relationships between the source and target languages. During this process, the model adjusts its parameters to better understand and translate the specialized terms and phrases used in your industry.
5. Test the model – After the model has been trained, it is essential to test its performance. Testing allows you to evaluate how well the model translates new, unseen text that was not part of the training data. By using a separate set of test documents, you can assess the model’s accuracy, fluency, and ability to handle specialized terminology.
6. Publish the model – Once you are satisfied with the model’s performance based on the testing phase, the next step is to publish the model. Publishing makes the model available for use in production environments. This means that the custom translation model can now be integrated into your applications, where it will provide real-time translations for your specialized content.

For detailed steps on creating the custom translation, refer to the Microsoft reference documentation.

Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.