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Microsoft DP-600 Exam Syllabus Topics:

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

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q129-Q134):

NEW QUESTION # 129
You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.
You suspect that some DAX queries load unnecessary columns into memory.
You need to identify the frequently used columns that are loaded into memory.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

Answer: A,C

Explanation:
The Vertipaq Analyzer tool (B) and querying the
$system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. References = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.


NEW QUESTION # 130
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1 and a user named User1.
You need to ensure that User1 can read all the data in Lakehouse1. The solution must follow the principle of least privilege.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: A,D

Explanation:
To ensure a user has read access to all data in a Microsoft Fabric lakehouse while following the Principle of Least Privilege (PoLP), you should use Item-level sharing rather than assigning a workspace role.
Workspace roles like Admin, Member, or Contributor grant broad permissions across all items in a workspace, which is often unnecessary for a data consumer.
Implementation Steps
1. Avoid Workspace Roles: Do not add the user as an Admin, Member, or Contributor to the workspace. This prevents them from managing items or writing data.
[D] 2. Use the Share Feature: Navigate to the specific lakehouse and select Share.
3. Configure Granular Permissions: In the sharing dialog, select the following based on the user's technical needs:
3a. Read all SQL endpoint data (ReadData): Required if the user will query data using T-SQL, SQL Server Management Studio (SSMS), or Power BI reports.
[B] 3b. Read all Apache Spark (ReadAll): Required if the user needs to read data via Spark notebooks, OneLake APIs, or the Lakehouse explorer.
3c.Build reports on the default semantic model (Build): Necessary if they need to create new Power BI reports using the lakehouse's data.
Reference:
https://data-mozart.com/understanding-data-access-options-in-microsoft-fabric/


NEW QUESTION # 131
You have a Fabric tenant that contains customer churn data stored as Parquet files in OneLake. The data contains details about customer demographics and product usage.
You create a Fabric notebook to read the data into a Spark DataFrame. You then create column charts in the notebook that show the distribution of retained customers as compared to lost customers based on geography, the number of products purchased, age. and customer tenure.
Which type of analytics are you performing?

Answer: C


NEW QUESTION # 132
You are the administrator of a Fabric workspace that contains a lakehouse named Lakehouse1.
Lakehouse1 contains the following tables:
Table1: A Delta table created by using a shortcut
Table2: An external table created by using Spark
Table3: A managed table
You plan to connect to Lakehouse1 by using its SQL endpoint.
What will you be able to do after connecting to Lakehouse1?

Answer: A


NEW QUESTION # 133
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.

You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:

Does this meet the goal?

Answer: A

Explanation:
The suggested replacement does not improve performance. COUNTROWS( ' Order Item ' ) < 0 is always false (row counts cannot be negative), so the NOT will always evaluate to true. This means the filter condition becomes non-selective and would return all customers, not just those with orders. Therefore, it does not meet the optimization goal.


NEW QUESTION # 134
......

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