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Microsoft DP-600 Exam Syllabus Topics:

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

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DP-600 Test Dumps: Implementing Analytics Solutions Using Microsoft Fabric & Implementing Analytics Solutions Using Microsoft Fabric Questions & Answers

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q136-Q141):

NEW QUESTION # 136
You are creating a semantic model in Microsoft Power Bl Desktop.
You plan to make bulk changes to the model by using the Tabular Model Definition Language (TMDL) extension for Microsoft Visual Studio Code.
You need to save the semantic model to a file.
Which file format should you use?

Answer: D

Explanation:
When saving a semantic model to a file that can be edited using the Tabular Model Scripting Language (TMSL) extension for Visual Studio Code, the PBIX (Power BI Desktop) file format is the correct choice. The PBIX format contains the report, data model, and queries, and is the primary file format for editing in Power BI Desktop. References = Microsoft's documentation on Power BI file formats and Visual Studio Code provides further clarification on the usage of PBIX files.


NEW QUESTION # 137
You create a semantic model by using Microsoft Power Bl Desktop. The model contains one security role named SalesRegionManager and the following tables:
* Sales
* SalesRegion
* Sales Ad dress
You need to modify the model to ensure that users assigned the SalesRegionManager role cannot see a column named Address in Sales Address.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 138
You have a Fabric notebook that has the Python code and output shown in the following exhibit.


Which type of analytics are you performing?

Answer: C

Explanation:
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of "what has happened" by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
References: Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.


NEW QUESTION # 139
You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace.
You need to use the model to generate predictions by using the PREDICT function in a Fabric notebook.
Which two languages can you use to perform model scoring? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

Answer: A,C

Explanation:
https://learn.microsoft.com/en-us/azure/synapse-analytics/machine-learning/tutorial-score-model- predict-spark-pool


NEW QUESTION # 140
You have a Fabric tenant that contains a lakehouse.
You plan to query sales data files by using the SQL endpoint. The files will be in an Amazon Simple Storage Service (Amazon S3) storage bucket.
You need to recommend which file format to use and where to create a shortcut.
Which two actions should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

Answer: A,C

Explanation:
You should use a columnar file format such as Parquet or ORC (Optimized Row Columnar).
These formats are highly optimized for analytical queries and provide efficient storage and query performance.
In the Tables section of your lakehouse, you define virtual tables that represent external data sources. These virtual tables can be backed by data stored externally in formats such as Parquet or ORC in Amazon S3.


NEW QUESTION # 141
......

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