Databricks Generative AI Engineer Associate Practice Test Overview
Total Questions: 60 (This number is an estimate and may vary in the actual exam)
Target Job Roles: This exam targets aspiring and current Data Engineers, Machine Learning Engineers, and Data Scientists seeking to demonstrate proficiency in utilizing Databricks for Generative AI applications. It's also beneficial for those aiming for roles involving AI model deployment and management within a Databricks ecosystem.
Language: English
This practice test simulates the real Databricks Generative AI Engineer Associate exam, helping you assess your readiness. Think of it as a final dress rehearsal before the big show!
I. Fundamentals of Generative AI: (15 Questions)
- A. Generative AI Models: Understanding different generative AI model architectures (e.g., Transformers, diffusion models). This section tests your conceptual understanding, not detailed mathematical derivations.
- B. Model Training and Evaluation: Know the basics of training data preparation, model training processes, and key evaluation metrics (e.g., BLEU score, FID). Think about it like teaching a dog a trick – you need the right training and the right way to measure success.
- C. Ethical Considerations: Recognizing potential biases in generative AI models and understanding responsible AI practices. This isn't just about building models; it's about building them responsibly.
II. Databricks Platform and Ecosystem: (15 Questions)
- A. Databricks Workspace Navigation: Familiarity with the Databricks interface, including notebooks, clusters, and data management tools. Imagine it's your digital garage where you build and manage your AI projects.
- B. Data Ingestion and Transformation: Working with diverse data sources (structured, unstructured) and using Databricks tools for data preprocessing and feature engineering. It's all about getting your data ready for the AI model, like prepping ingredients for a delicious meal.
- C. Spark and Python Proficiency: Practical application of Spark and Python within the Databricks environment for generative AI tasks. This is where you put your coding skills to the test!
III. Deployment and Monitoring of Generative AI Models: (15 Questions)
- A. Model Deployment Strategies: Understanding different approaches to deploying generative AI models on Databricks (e.g., batch inference, real-time inference). Think of deploying a model like launching a rocket – you need a plan!
- B. Model Monitoring and Maintenance: Techniques for monitoring model performance, detecting drift, and retraining models as needed. Just like a car needs regular maintenance, so do AI models.
- C. Scalability and Optimization: Strategies for scaling generative AI models on the Databricks platform to handle increasing workloads. It’s about making sure your AI can handle the load as it grows, like a well-designed city's infrastructure.
IV. Practical Application and Case Studies: (15 Questions)
- A. Real-world scenarios: Applying generative AI techniques to solve problems in various domains (e.g., text generation, image generation, code generation). This is where you'll see how all the pieces fit together.
- B. Case study analysis: Analyzing real-world use cases of Generative AI on Databricks and identifying best practices. Learn from the successes (and failures) of others. Remember, even seasoned professionals learn from mistakes.
- C. Problem-solving and troubleshooting: Identifying and resolving common issues related to deploying and managing generative AI models in Databricks. This tests your problem-solving skills – a crucial skill for any engineer.
Remember: This is a practice test overview. The actual exam may cover additional topics or have slightly different weighting. Good luck with your preparations!
