Databricks Performance Optimization - Mandarin Chinese

提示公司 AI Assistant 的两部分基础课程。第一部分（基础知识）阐释提示的具体程度如何影响回答质量，将该助手定位为一位才华横溢但严格按字面理解指令、通过 RAG 架构扎根于内部知识的"实习生"，介绍其可访问与不可访问的内容，强调"信任但需核实"的思维方式，并引入 C-O-I-E 框架（Context、Outcome、Instruction、Example）。第二部分（技巧）构建提示技巧工具箱，并讲授如何在各技巧之间做出选择：将任务与方法相匹配、内容生成（零样本与少样本）、推理（思维链、自问自答、元提示）、工作流（提示链接以及何时使用链接而非单一提示），以及如何清晰地组织提示结构。

Databricks Performance Optimization teaches data engineers and analysts how to diagnose, measure, and fix performance bottlenecks on the Databricks Data Intelligence Platform, and how to tie performance improvements back to cost. The course follows a measurement-first, platform-first workflow: let the platform's automatic optimizations do the heavy lifting, verify what was applied using Query Profile and system tables, then manually tune where needed.

Learners build a foundation for measuring performance with Query Profile, Performance Insights, and query history system tables. From there, they explore key optimization techniques, including data layout and self-tuning managed tables, liquid clustering, caching and intermediate results, shuffle, skew, spill, row explosions, driver performance, Python UDFs, serverless compute, Photon, and cost attribution.

Throughout the course, learners explore deliberately slow queries over synthetic retail data and see how different optimization techniques affect performance. They use evidence such as file pruning, task execution time, Photon coverage, and cost to evaluate improvements. Two scenario-based labs reinforce the optimization workflow from diagnosis through validation.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

This course covers what an administrator of a Databricks account and its workspaces needs to know, cloud-agnostic: platform architecture and admin roles, compute resource administration, identity administration, CI/CD tooling administration, and workspace administration and security. Cloud-specific mechanics (IAM, storage, networking) are covered in the companion cloud admin courses. Prepares learners for the Databricks Accredited Platform Administrator accreditation.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.