Microsoft DP-600日本語 Exam Details & Actual Exam Questions

Exam Code/Number: DP-600日本語
Exam Name/Title: Implementing Analytics Solutions Using Microsoft Fabric (DP-600日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified

Exam Questions: 203
Updated On: Sep,04 2026
Certification Level: Associate

Microsoft Implementing Analytics Solutions Using Microsoft Fabric (DP-600日本語版) Exam Questions

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Microsoft DP-600日本語 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Implementing Analytics Solutions Using Microsoft Fabric
Exam Number:	DP-600
Passing Score:	700/1000
Real Exam Qty:	40-60
Related Certifications:	Microsoft Certified: Fabric Analytics Engineer Associate
Certificate Validity Period:	1 year
Exam Format:	Drag-and-drop, Case studies, Multiple-choice
Exam Price:	$165 USD
Available Languages:	Japanese, Korean, English, Chinese (Simplified)
Exam Duration:	120 minutes
Sample Questions:	Microsoft DP-600日本語 Sample Questions
Exam Way:	Online proctored or in-person testing center
Pre Condition:	Candidates should have foundational knowledge of data concepts, experience with Microsoft Fabric, and proficiency in data transformation and modeling. Familiarity with Power BI is recommended but not required.
Official Syllabus URL:	https://learn.microsoft.com/en-us/certifications/exams/dp-600

Microsoft DP-600日本語 Exam Syllabus Topics:

Section	Weight	Objectives
Design and manage the data model	20-25%	- Implement and configure a data model 1. Configure SQL analytics endpoint 2. Create and manage semantic models 3. Use DirectLake mode for large datasets 4. Implement row-level security (RLS) - Design a data model 1. Choose appropriate data model type (lakehouse vs warehouse) 2. Define relationships and hierarchies 3. Design a star or snowflake schema 4. Implement dimension and fact tables
Clean, transform, and enrich data	25-30%	- Transform data 1. Implement data standardization and normalization 2. Perform schema evolution and mapping 3. Use Spark libraries for data transformation 4. Use Dataflow Gen2 for transformations - Clean data 1. Handle missing values and duplicates 2. Validate data quality using Data Quality Profiling 3. Apply data cleansing techniques - Enrich data 1. Merge and join data sources 2. Implement slowly changing dimensions (SCD) 3. Implement incremental data loading
Deploy and maintain a data solution	10-15%	- Maintain a data solution 1. Optimize query performance 2. Manage workspace and capacity settings 3. Implement data refresh strategies - Deploy data assets 1. Automate deployments using APIs and scripts 2. Implement CI/CD for Fabric items 3. Use deployment pipelines for development to production
Load and prepare data	20-25%	- Create and configure items 1. Create and configure Lakehouse, Warehouse, or data pipeline 2. Create and configurehortcuts 3. Configure data processing with notebooks - Ingest data from source systems 1. Ingest data using PySpark or Spark SQL 2. Use Data Factory copy activity for batch ingestion 3. Configure Data Gateway for hybrid scenarios 4. Use Data Factory data flow for transformation 5. Implement streaming data ingestion with Eventstream
Secure and monitor data solutions	15-20%	- Secure data solutions 1. Use Microsoft Purview for data governance 2. Implement column-level and row-level security 3. Configure sensitive data classifications 4. Configure workspace and item permissions - Monitor data solutions 1. Implement alerting and notifications 2. Use OneLake monitoring capabilities 3. Monitor pipeline and dataflow execution 4. Review and analyze capacity metrics