Use this quick start guide to collect all the information about Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCA Fabric Data Engineer certification exam.
The Implementing Data Engineering Solutions Using Microsoft Fabric certification is mainly targeted to those candidates who want to build their career in Microsoft Fabric domain. The Microsoft Certified - Fabric Data Engineer Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCA Fabric Data Engineer.
Implementing Data Engineering Solutions Using Microsoft Fabric Exam Summary:
| Exam Name | Microsoft Certified - Fabric Data Engineer Associate |
| Exam Code | DP-700 |
| Exam Price | $165 (USD) |
| Duration | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | DP-700T00-A: Microsoft Fabric Data Engineer |
| Schedule Exam | Pearson VUE |
| Sample Questions | Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions |
| Practice Exam | Microsoft DP-700 Certification Practice Exam |
Microsoft DP-700 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Implement and manage an analytics solution (30–35%) |
|
| Configure Microsoft Fabric workspace settings |
- Configure Spark workspace settings - Configure domain workspace settings - Configure OneLake workspace settings - Configure data workflow workspace settings |
| Implement lifecycle management in Fabric |
- Configure version control - Implement database projects - Create and configure deployment pipelines |
| Configure security and governance |
- Implement workspace-level access controls - Implement item-level access controls - Implement row-level, column-level, object-level, and folder/file-level access controls - Implement dynamic data masking - Apply sensitivity labels to items - Endorse items - Implement and use workspace logging |
| Orchestrate processes |
- Choose between a pipeline and a notebook - Design and implement schedules and event-based triggers - Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions |
Ingest and transform data (30–35%) |
|
| Design and implement loading patterns |
- Design and implement full and incremental data loads - Prepare data for loading into a dimensional model - Design and implement a loading pattern for streaming data |
| Ingest and transform batch data |
- Choose an appropriate data store - Choose between dataflows, notebooks, KQL, and T-SQL for data transformation - Create and manage shortcuts to data - Implement mirroring - Ingest data by using pipelines - Transform data by using PySpark, SQL, and KQL - Denormalize data - Group and aggregate data - Handle duplicate, missing, and late-arriving data |
| Ingest and transform streaming data |
- Choose an appropriate streaming engine - Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence - Process data by using eventstreams - Process data by using Spark structured streaming - Process data by using KQL - Create windowing functions |
Monitor and optimize an analytics solution (30–35%) |
|
| Monitor Fabric items |
- Monitor data ingestion - Monitor data transformation - Monitor semantic model refresh - Configure alerts |
| Identify and resolve errors |
- Identify and resolve pipeline errors - Identify and resolve dataflow errors - Identify and resolve notebook errors - Identify and resolve eventhouse errors - Identify and resolve eventstream errors - Identify and resolve T-SQL errors |
| Optimize performance |
- Optimize a lakehouse table - Optimize a pipeline - Optimize a data warehouse - Optimize eventstreams and eventhouses - Optimize Spark performance - Optimize query performance |
To ensure success in Microsoft MCA Fabric Data Engineer certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) exam.