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Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Secure and monitor data solutions15-20%- Secure data solutions
  • 1. Implement column-level and row-level security
  • 2. Use Microsoft Purview for data governance
  • 3. Configure workspace and item permissions
  • 4. Configure sensitive data classifications
- Monitor data solutions
  • 1. Monitor pipeline and dataflow execution
  • 2. Use OneLake monitoring capabilities
  • 3. Implement alerting and notifications
  • 4. Review and analyze capacity metrics
Load and prepare data20-25%- 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. Use Data Factory copy activity for batch ingestion
  • 2. Configure Data Gateway for hybrid scenarios
  • 3. Use Data Factory data flow for transformation
  • 4. Implement streaming data ingestion with Eventstream
  • 5. Ingest data using PySpark or Spark SQL
Deploy and maintain a data solution10-15%- Deploy data assets
  • 1. Automate deployments using APIs and scripts
  • 2. Use deployment pipelines for development to production
  • 3. Implement CI/CD for Fabric items
- Maintain a data solution
  • 1. Implement data refresh strategies
  • 2. Optimize query performance
  • 3. Manage workspace and capacity settings
Design and manage the data model20-25%- Design a data model
  • 1. Define relationships and hierarchies
  • 2. Choose appropriate data model type (lakehouse vs warehouse)
  • 3. Implement dimension and fact tables
  • 4. Design a star or snowflake schema
- Implement and configure a data model
  • 1. Implement row-level security (RLS)
  • 2. Create and manage semantic models
  • 3. Configure SQL analytics endpoint
  • 4. Use DirectLake mode for large datasets
Clean, transform, and enrich data25-30%- Enrich data
  • 1. Merge and join data sources
  • 2. Implement slowly changing dimensions (SCD)
  • 3. Implement incremental data loading
- Transform data
  • 1. Use Dataflow Gen2 for transformations
  • 2. Implement data standardization and normalization
  • 3. Use Spark libraries for data transformation
  • 4. Perform schema evolution and mapping
- Clean data
  • 1. Handle missing values and duplicates
  • 2. Apply data cleansing techniques
  • 3. Validate data quality using Data Quality Profiling

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q181-Q186):

NEW QUESTION # 181
You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.

You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?

Answer: D


NEW QUESTION # 182
You have a Fabric tenant that contains a lakehouse named Lakehouse1.
You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.
What should you configure? (5)

Answer: A

Explanation:
To prevent new tables added to Lakehouse1 from being automatically added to the default semantic model, you should configure the semantic model settings. There should be an option within the settings of the semantic model to include or exclude new tables by default. By adjusting these settings, you can control the automatic inclusion of new tables.
References: The management of semantic models and their settings would be covered under the documentation for the semantic layer or modeling features of the Fabric tenant's lakehouse solution.


NEW QUESTION # 183
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?

Answer: D


NEW QUESTION # 184
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1. Report1 includes a Python visual. Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed.
You need all rows to appear in the visual. What should you do?

Answer: A

Explanation:
To ensure all rows appear in the Python visual within a Power BI report, option C, adding a unique field to each row, is the correct solution. This will prevent automatic grouping by unique values and allow for all instances of data to be represented in the visual. References = For more on Power BI Python visuals and how they handle data, please refer to the Power BI documentation.


NEW QUESTION # 185
You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?

Answer: D

Explanation:
Lookup activity reads and returns the content of a configuration file or table. It also returns the result of executing a query or stored procedure. The output can be a singleton value or an array of attributes, which can be consumed in a subsequent copy, transformation, or control flow activities like ForEach activity.
https://learn.microsoft.com/en-us/azure/data-factory/control-flow-lookup-activity


NEW QUESTION # 186
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