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Key Skills and Knowledge Areas for Azure AI Engineer Certification

To earn the Azure AI Engineer Certification, you need a thorough understanding of various AI services offered by Microsoft Azure. Below are the key skills and knowledge areas that the certification focuses on:

• Natural Language Processing (NLP): Understanding and implementing NLP techniques to process text data, sentiment analysis, and language translation using Azure services like Text Analytics and Azure Cognitive Services.

• Computer Vision: Leveraging Azure's computer vision capabilities to analyze visual data, including image recognition, facial recognition, and object detection.

• Speech Services: Working with Azure Speech Services to convert spoken language into text and vice versa, including speech synthesis and speech recognition.

• Bots and Conversational AI: Building and deploying intelligent chatbots using Azure Bot Service and QnA Maker, integrating conversational AI solutions into applications.

• Machine Learning (ML) Models: Developing, training, and deploying machine learning models using Azure Machine Learning, understanding the end-to-end ML lifecycle, including data preparation, model training, and deployment.

• Security and Compliance: Ensuring AI solutions are secure, compliant, and adhere to Azure's best practices for data protection and governance.

• Integration of AI Solutions: Integrating AI services into existing applications and workflows to enhance business processes, improve efficiency, and deliver intelligent insights.

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