Topics to study for Microsoft’s DP-700 exam

In this article, we’ll have a look at the topics that you need to study for Microsoft’s DP-700 “Implementing Data Engineering Solutions Using Microsoft Fabric” exam.

If you want to learn using a video course, click here for our DP-700 “Implementing Data Engineering Solutions Using Microsoft Fabric” video course.

Configure Microsoft Fabric workspace settings

Implement lifecycle management in Fabric

  • Configure version control. This is covered in the DP-600 exam. Set up and manage version control systems to track and manage changes in Microsoft Fabric projects
  • Implement database projects. Understand how to design, develop, and deploy database projects using tools like SQL scripts and database templates within Fabric.
  • Create and configure deployment pipelines. Gain expertise in setting up deployment pipelines to automate the release of data workflows, ensuring consistency across development, testing, and production environments.

Configure security and governance

  • Implement workspace-level access controls. Configure permissions at the workspace level to manage access for users and groups effectively across Fabric environments.
  • Implement item-level access controls. Apply specific access controls to individual items, such as datasets, notebooks, or pipelines, for granular security.
  • Implement row-level, column-level, object-level, and file-level access controls. Implement fine-grained access controls to secure data at the row, column, object, or file level, ensuring data security and compliance
  • Implement dynamic data masking. Obscure sensitive information dynamically based on user roles or access levels.
  • Apply sensitivity labels to items. Assign sensitivity labels to items like datasets and reports to classify and protect data in line with organizational policies.
  • Endorse items. Endorse items in Fabric (e.g., certified or promoted datasets) to signal quality and reliability for organizational use.

Orchestrate processes

  • Choose between a pipeline and a notebook (additional link). Decide when to use a pipeline for orchestration and workflow automation or a notebook for advanced data processing and custom logic.
  • Design and implement schedules and event-based triggers (additional link). Configure schedules for periodic task execution and event-based triggers to automate workflows based on specific actions or conditions.
  • Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions. Integrate notebooks and pipelines, using parameters and dynamic expressions for flexible and efficient workflows.

Design and implement loading patterns

  • Design and implement full and incremental data loads. Create efficient full and incremental loading patterns for batch data, ensuring data pipelines handle large-scale data updates effectively.
  • Prepare data for loading into a dimensional model. Understand the processes to transform and structure raw data into a dimensional model, optimizing it for analytics and reporting.
  • Design and implement a loading pattern for streaming data. Ingest, transform, and load real-time data into destinations like Lakehouses, Warehouses, or KQL databases. Create efficient full and incremental loading patterns for batch data, ensuring data pipelines handle large-scale data updates effectively.

Ingest and transform batch data

  • Choose an appropriate data store. Select the best data store (e.g., Lakehouse, Warehouse, or KQL Database) based on performance, scalability, and use case requirements.
  • Choose between dataflows, notebooks, and T-SQL for data transformation. Understand when to use dataflows for ETL, notebooks for advanced transformations, or T-SQL for structured querying, based on the data and task complexity.
  • Create and manage shortcuts to data. Provide simplified, centralized access to datasets across different workspaces in Fabric.
  • Implement mirroring. Synchronize and replicate datasets between environments for consistency and high availability.
  • Ingest data by using pipelines. Automate data ingestion workflows from various sources into Fabric destinations.
  • Transform data by using PySpark, SQL, and KQL. Use PySpark for big data processing, SQL for structured transformations, and KQL for querying real-time analytics data.
  • Denormalize data. Flatten relational datasets to improve querying performance and simplify data models.
  • Group and aggregate data. Summarize, report, and analyse using Fabric tools like SQL, PySpark, or KQL.
  • Handle duplicate, missing, and late-arriving data. Ensure data quality and accuracy in real-time and batch processing workflows.

Ingest and transform streaming data

Monitor Fabric items

Identify and resolve errors

Optimize performance

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