GenAI - LLMOps Test

This skill focuses on the practical application of transfer learning and the customization of pre-trained models. The test assesses proficiency in advanced fine-tuning techniques, such as parameter freezing and gradient accumulation, using tools like Hugging Face. This is crucial for tailoring models to meet specific business needs and improving their performance on proprietary datasets.

This skill covers the design and implementation of complex NLP pipelines, including tasks like token classification and sentiment analysis. The test evaluates the candidate's ability to execute both basic and advanced NLP tasks, such as zero-shot learning and intent recognition, in real-world applications. Proficiency in this area ensures the candidate can develop and manage NLP workflows effectively.

This skill tests expertise in deploying and managing LLMs using cloud-native services. The evaluation focuses on containerization, REST API deployment, and model versioning, essential for scalable, low-latency applications. Candidates must demonstrate their ability to use platforms like AWS SageMaker or Google AI Platform to deploy models efficiently.

This skill involves advanced techniques for improving model performance, such as quantization and pruning. The test assesses understanding of computational efficiency and cost trade-offs, which are vital for optimizing models in production environments. Mastery of this skill ensures that candidates can enhance model performance while managing resource utilization effectively.

This skill explores the end-to-end management of LLM pipelines, emphasizing CI/CD integration and production monitoring. The test evaluates the candidate's ability to use tools like MLflow to ensure model reliability and performance. Proficiency in MLOps is crucial for maintaining operational excellence and managing AI systems at scale.

This skill assesses proficiency in multi-cloud and hybrid cloud orchestration. The test evaluates knowledge of scaling compute resources, cost optimization, and setting up fault-tolerant systems for LLM training and deployment. Mastery of this skill ensures high availability and efficient resource management in large-scale AI projects.

This skill tests understanding of fairness, accountability, and transparency in AI systems. The evaluation covers bias mitigation strategies and compliance with legal standards like GDPR. Proficiency in this area is crucial for developing AI solutions that are ethically sound and legally compliant.

This skill evaluates the development of complex, domain-specific applications using LLMs. The test assesses the candidate's ability to integrate LLMs with external APIs and custom workflows, essential for creating sophisticated solutions in industries like healthcare and finance.

This skill focuses on deployment strategies for managing Power BI reports and Fabric solutions. The test evaluates the candidate's ability to create deployment pipelines and optimize Fabric capacity, ensuring effective deployment and administration of enterprise solutions.